Information Processing Apparatus, Control Method, Program, and Storage Medium

Abstract

To provide an information processing device capable of appropriately creating correction information related to predicted water level information.SOLUTION: A controller 13 of an information processing device 1A or an information processing device 1B calculates a marine vessel reference height as a height of a reference position of a marine vessel on the basis of measurement data for a ground object measured by a Lidar 3 provided on the marine vessel. Then, the controller 13 calculates a measured water level representing the height of a water surface on the basis of water measurement data, which is measurement data for the water surface measured by the Lidar 3, and the marine vessel reference height. Then, the controller 13 creates water level correction information related to the predicted water level information on the basis of the predicted water level represented by the predicted water level information representing the predicted water level at each water level prediction point in a river and a measured water level calculated at the water level prediction point corresponding to the predicted water level or a point nearest to the water level prediction point.SELECTED DRAWING: Figure 17

Description

[Technical Field]

[0001] This disclosure relates to corrections for predicted river water levels. [Background technology]

[0002] Conventionally, techniques have been known for estimating the self-position of a moving object by comparing (matching) shape data of surrounding objects measured using measuring devices such as laser scanners with map information in which the shapes of surrounding objects are stored in advance. For example, Patent Document 1 discloses an autonomous mobile system that determines whether a detected object in a voxel, which is divided into a space according to a predetermined rule, is a stationary or moving object, and performs matching between map information and measurement data for voxels in which stationary objects exist. Patent Document 2 also discloses a scan matching method that estimates the self-position by comparing voxel data, which includes the mean vector and covariance matrix of stationary objects for each voxel, with point cloud data output by a lidar. Furthermore, Patent Document 3 describes a method for controlling the attitude of a ship in an automatic docking device that performs automatic docking of ships, so that light emitted from a lidar is reflected by objects around the docking position and received by the lidar. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] International release WO2013 / 076829 [Patent Document 2] International release WO2018 / 221453 [Patent Document 3] Japanese Patent Publication No. 2020-59403 [Overview of the project] [Problems that the invention aims to solve]

[0004] When determining the operation route, it is common to select a route that can safely pass through the bridge based on predicted water level information and the like. However, whether the ship can actually pass through needs to be confirmed before approaching the bridge, taking into account cases where the water level is higher than the predicted water level. And if it is determined in advance that there is a risk of passing through the bridge, it is possible to take measures such as turning back in a situation where the river width is wide and there are no other ships, or changing the route. In order to accurately make such a determination, it is necessary to accurately correct the predicted water level.

[0005] The present disclosure has been made to solve the above problems, and one of the main objects is to provide an information processing apparatus capable of suitably generating correction information regarding predicted water level information.

Means for Solving the Problems

[0006] The invention according to the claim is a ship reference height calculation means for calculating a ship reference height, which is the height of the reference position of the ship, based on the measurement data of the ground object measured by the measuring device provided on the ship; a measured water level calculation means for calculating a measured water level representing the height of the water surface based on the water surface measurement data, which is the measurement data of the water surface measured by the measuring device, and the ship reference height; a correction information generation means for generating correction information regarding the predicted water level information based on the predicted water level represented by the predicted water level information for each water level prediction point in the river and the measured water level calculated at the water level prediction point corresponding to the predicted water level or the point closest to the water level prediction point; and an information processing apparatus having the above.

[0007] Further, the invention according to the claim is a control method executed by a computer, calculating a ship reference height, which is the height of the reference position of the ship, based on the measurement data of the ground object measured by the measuring device provided on the ship, calculating a measured water level representing the height of the water surface based on the water surface measurement data, which is the measurement data of the water surface measured by the measuring device, and the ship reference height, Correction information for the predicted water level information is generated based on the predicted water level information, which represents the predicted water level for each water level prediction point in a river, and the measured water level calculated at the water level prediction point corresponding to the said predicted water level or the point closest to the said water level prediction point. This is a control method.

[0008] Furthermore, the invention described in the claims is, Based on the measurement data of geographical features measured by measuring devices installed on the vessel, the ship's reference height, which is the height of the vessel's reference position, is calculated. Based on the water surface measurement data, which is the water surface measurement data measured by the measuring device, and the ship's reference height, the measured water level, which represents the height of the water surface, is calculated. This program causes a computer to perform a process to generate correction information for the predicted water level information, based on the predicted water level represented by the predicted water level information, which represents the predicted water level for each water level prediction point in a river, and the measured water level calculated at the water level prediction point corresponding to the said predicted water level or the point closest to the said water level prediction point. [Brief explanation of the drawing]

[0009] [Figure 1] This is a schematic diagram of the flight support system. [Figure 2] This diagram illustrates the field of view of the ship and lidar included in the navigation support system. [Figure 3] (A) This is a block diagram showing an example of the hardware configuration of an information processing device. (B) This is a block diagram showing an example of the hardware configuration of a server device. [Figure 4] This map clearly shows the departure and destination points of ships, as well as bridges located on rivers. [Figure 5] This is an overhead view showing the area around a bridge located on a proposed route. [Figure 6] (A) A graph showing the trend of predicted water levels at water level prediction points. (B) A graph showing the trend of predicted water levels at water level prediction points and a graph showing the trend of predicted water levels at bridge crossing points. [Figure 7]This diagram shows a ship as observed from the rear, assuming the ship is located at the point where it passes under a bridge. [Figure 8] This table shows the estimated time of passage, predicted water level, and predicted interval for each bridge along the proposed route. [Figure 9] This is an example of a controller function block related to the flight route determination process. [Figure 10] This is an example of a flowchart for determining flight routes. [Figure 11] This is a block diagram of the information processing device according to the second embodiment. [Figure 12] This is an overhead view showing the area around bridges located along the flight route. [Figure 13] This is a view of a vessel from the rear, when registered features are within the measurement range of the lidar. [Figure 14] (A) This shows the time change between the predicted water level and the measured water level at the location where the vessel is located. (B) This is an example of the frequency distribution of multiple water level difference values ​​calculated within the immediately preceding predetermined period. [Figure 15] This table shows the estimated time of passage, bridge-corrected water level, and predicted interval for each bridge located on a shipping route that has not yet been traversed by a vessel. [Figure 16] This is an example of a functional block of the predictive water level correction unit related to the generation of water level correction information in the second embodiment. [Figure 17] This is an example flowchart showing the procedure for generating water level correction information in the second embodiment. [Figure 18] This is a block diagram of an information processing device according to the third embodiment. [Figure 19] This diagram shows the position of a ship in three-dimensional Cartesian coordinates. [Figure 20] An example of a general data structure for voxel data is shown. [Figure 21] This is an example of a functional block diagram for the self-localization unit. [Figure 22] This is a view of a ship from the rear. [Figure 23](A) This shows the time change between the predicted water level and the measured water level at the location where the vessel is located. (B) This is an example of the frequency distribution of multiple water level difference values ​​calculated within the immediately preceding predetermined period. [Figure 24] This is an example of a functional block of the predictive water level correction unit related to the generation of water level correction information in the third embodiment. [Figure 25] This is an example flowchart showing the procedure for generating water level correction information in the third embodiment. [Figure 26] This is a schematic configuration of the flight support system according to the fourth embodiment. [Figure 27] This is a block diagram showing an example of the hardware configuration of a server device according to the fourth embodiment. [Figure 28] (A) An example of the data structure for a predicted water level database. (B) An example of the data structure for measured water level information. [Figure 29] This is an example of a functional block of the controller of the server device according to the fourth embodiment. [Figure 30] (A) An example of a flowchart executed by the information processing device according to the third embodiment. (B) An example of a flowchart executed by the server device according to the third embodiment. [Modes for carrying out the invention]

[0010] According to a preferred embodiment of the present invention, the information processing device includes: a ship reference height calculation means for calculating a ship reference height, which is the height of the ship's reference position, based on measurement data of features measured by a measuring device installed on the ship; a measured water level calculation means for calculating a measured water level representing the height of the water surface based on water surface measurement data, which is measurement data of the water surface measured by the measuring device, and the ship reference height; and a correction information generation means for generating correction information relating to the predicted water level information based on the predicted water level represented by predicted water level information, which represents the predicted water level for each water level prediction point in a river, and the measured water level calculated at the water level prediction point corresponding to the predicted water level or the point closest to the water level prediction point. According to this embodiment, the information processing device can calculate the measured water level based on measurement data of features measured by a measuring device and suitably generate correction information relating to the predicted water level information.

[0011] In one embodiment of the above-described information processing device, the ship reference elevation calculation means estimates the position of the ship based on the measurement data of the feature and the map data relating to the feature, and obtains the result of the position estimation in the height direction as the ship reference elevation. According to this embodiment, the information processing device can suitably utilize the self-position estimation result as the ship reference elevation.

[0012] In another embodiment of the information processing device described above, the correction information generation means generates the correction information based on the difference between the measured water level and the predicted water level, which is calculated from the ship's reference elevation based on the position estimation results at the water level prediction point or the point closest to the water level prediction point. In this embodiment, the information processing device can suitably generate correction information for correcting the predicted water level.

[0013] In another embodiment of the information processing device described above, the map data is voxel data representing the position of an object in each voxel, which is a unit area, and the ship reference elevation calculation means performs the position estimation based on a comparison of the voxel data with the measurement data of the features. In this embodiment, the information processing device can accurately perform ship position estimation.

[0014] In another embodiment of the information processing device described above, the ship's reference elevation calculation means calculates the ship's reference elevation based on the height distance between the feature and the reference position calculated based on the measurement data of the feature, and the height of the feature based on map data relating to the feature. In this embodiment, the information processing device can accurately calculate the ship's reference elevation without relying on the self-position estimation result.

[0015] In another embodiment of the information processing device described above, the correction information generation means calculates a continuous interpolated measured water level along the ship's operating route by interpolating the measured water levels calculated at a plurality of points, and generates the correction information based on the difference between the interpolated measured water level and the predicted water level at the water level prediction point. In this embodiment, the information processing device can suitably generate correction information for correcting the predicted water level, regardless of the self-position estimation result.

[0016] In another embodiment of the above-described information processing device, the information processing device further includes a bridge water level calculation means that calculates the bridge water level, which is the water level at the bridge location where the bridge exists, based on the correction information and the predicted water level. In this embodiment, the information processing device can use the generated correction information to calculate the accurate water level at the bridge location.

[0017] In another embodiment of the above-described information processing device, the information processing device further includes a bridge passage feasibility determination means for determining whether a vessel can pass under a bridge it is scheduled to pass under, based on the bridge water level, the water surface distance which is the height distance between the reference position and the water surface based on the water surface measurement data, the height from the reference position to the highest point of the vessel, and the height of the bridge based on map data. In this embodiment, the information processing device can accurately determine whether a vessel can pass under a bridge by utilizing the generated correction information.

[0018] According to another preferred embodiment of the present invention, a control method executed by a computer is provided, which calculates a ship reference height, which is the height of the ship's reference position, based on measurement data of features measured by a measuring device installed on the ship; calculates a measured water level, which represents the height of the water surface, based on water surface measurement data, which is measurement data of the water surface measured by the measuring device, and the ship reference height; and generates correction information relating to the predicted water level information, based on the predicted water level represented by predicted water level information, which represents the predicted water level for each water level prediction point in the river, and the measured water level calculated at the water level prediction point corresponding to the predicted water level or the point closest to the water level prediction point. By executing this control method, the computer can suitably calculate the measured water level based on measurement data of features measured by the measuring device and generate correction information relating to the predicted water level information.

[0019] According to yet another preferred embodiment of the present invention, the present invention is a program that causes a computer to perform the following processes: calculate a ship reference height, which is the height of the ship's reference position, based on measurement data of geographical features measured by a measuring device installed on the ship; calculate a measured water level, which represents the height of the water surface, based on water surface measurement data, which is measurement data of the water surface measured by the measuring device, and the ship reference height; and generate correction information related to the predicted water level information, based on the predicted water level represented by predicted water level information, which represents the predicted water level for each predicted water level point in the river, and the measured water level calculated at the water level prediction point corresponding to the predicted water level or the point closest to the water level prediction point. By executing this program, the computer can calculate the measured water level based on measurement data of geographical features measured by the measuring device and generate correction information related to the predicted water level information. Preferably, the above program is stored in a storage medium. [Examples]

[0020] Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings.

[0021] <First Example> (1-1) Overview of the flight support system Figures 1 and 2 show a schematic configuration of the navigation support system according to the first embodiment. Specifically, Figure 1 shows a block diagram of the navigation support system, Figure 2(A) is a top view illustrating the field of view (measuring range) 90 of the vessel and the lidar 3 described later, which are included in the navigation support system, and Figure 2(B) is a rear view showing the field of view 90 of the vessel and the lidar 3. The navigation support system includes an information processing device 1 that moves together with the vessel, which is a mobile object, a group of sensors 2 mounted on the vessel, and a server device 7.

[0022] The information processing device 1 is electrically connected to the sensor group 2 and provides operational support for the vessel equipped with the information processing device 1 based on the outputs of various sensors included in the sensor group 2 and the predicted water level information "D1" transmitted from the server device 7, as described below. In this embodiment, the information processing device 1 determines the vessel's route by accurately determining in advance whether the vessel can pass under a bridge. Operational support may also include berthing support such as automatic docking. The information processing device 1 may be a navigation device installed on the vessel or an electronic control device built into the vessel.

[0023] Sensor group 2 includes various external and internal sensors installed on the vessel. In this embodiment, sensor group 2 includes, for example, a Lidar (Light Detection and Ranging, or Laser Illuminated Detection and Ranging) 3 and a GPS (Global Positioning Satellite) receiver 5. Note that sensor group 2 may also have a receiver that generates positioning results from a GNSS other than GPS instead of the GPS receiver 5. The information processing device 1 obtains the vessel's position on the water surface, which is necessary when referring to the river map database described later, from the GPS receiver 5 or the like.

[0024] LIDA 3 is an external sensor that discretely measures the distance to an object in the outside world by emitting a pulsed laser within a predetermined angular range in the horizontal direction (see Figure 2(A)) and a predetermined angular range in the vertical direction (i.e., the direction of elevation and depression angles) (see Figure 2(B)), and generates three-dimensional point cloud data indicating the position of the object. In the examples in Figures 2(A) and 2(B), LIDA 3 is installed on the ship, with one LIDA pointed towards the left side of the ship and another LIDA pointed towards the right side of the ship. Note that the number of LIDA 3 installed on the ship is not limited to two; it may be one or three or more. LIDA 3 has an irradiation unit that irradiates laser light while changing the irradiation direction, a light receiving unit that receives reflected light (scattered light) of the irradiated laser light, and an output unit that outputs scan data based on the received signal output by the light receiving unit. The data measured for each direction (scanning position) of laser light irradiation is generated based on the irradiation direction corresponding to the laser light received by the light receiving unit and the response delay time of the laser light, which is determined based on the received signal described above. Note that the lidar 3 is not limited to the scan-type lidar described above, but may also be a flash-type lidar that generates 3D data by diffusing laser light into the field of view of a 2D array sensor. The lidar 3 is an example of a "measuring device" in the present invention.

[0025] Furthermore, in this embodiment, the vertical range measured by the lidar 3 includes at least the area above the horizontal (i.e., the direction in which the elevation angle is positive) and the area below the horizontal (i.e., the direction in which the depression angle is positive). As a result, the measurement range of the lidar 3 includes both the bridge when the ship passes over it and the water surface on which the ship is floating. If there are multiple lidars 3, it is sufficient that the measurement range of at least one lidar 3 includes the area above the horizontal and the measurement range of at least one lidar 3 includes the area below the horizontal.

[0026] The server device 7 transmits predicted water level information D1, which represents the predicted water level (also called "predicted water level") at multiple points on the river a predetermined time in advance, to the information processing device 1. Hereafter, each point where the predicted water level is calculated will also be called a "water level prediction point." For example, when the server device 7 receives request information from the information processing device 1, including the current location of the information processing device 1, it transmits predicted water level information D1, which represents the predicted water level at a water level prediction point located within a predetermined distance from the current location, to the information processing device 1. In another example, when the server device 7 receives information from the information processing device 1 specifying a river on which the information processing device 1 is currently operating or is scheduled to operate, it transmits predicted water level information D1 for the water level prediction point on the specified river to the information processing device 1. Note that the water level (water surface height) varies depending on the location on the river, and the phase of the change in water level over time also differs depending on the location on the river.

[0027] (1-2) Device configuration Figure 3(A) is a block diagram showing an example of the hardware configuration of the information processing device 1. The information processing device 1 mainly comprises an interface 11, a memory 12, and a controller 13. Each of these elements is interconnected via a bus line.

[0028] Interface 11 performs interface operations related to the exchange of data between the information processing device 1 and external devices. In this embodiment, Interface 11 acquires output data from each sensor in the sensor group 2, such as the lidar 3 and GPS receiver 5, and supplies the acquired data to the controller 13. Interface 11 also receives predicted water level information D1 from the server device 7 and supplies the predicted water level information D1 to the controller 13. Interface 11 also supplies signals related to ship control generated by the controller 13 to each component of the ship that controls the ship's operation. For example, a ship is equipped with a drive source such as an engine or electric motor, a propeller that generates thrust in the direction of travel based on the driving force of the drive source, a thruster that generates thrust in the lateral direction based on the driving force of the drive source, and a rudder, etc., which is a mechanism for freely determining the direction of travel of the ship. During automatic operation such as automatic docking, Interface 11 supplies control signals generated by the controller 13 to each of these components. If the ship is equipped with an electronic control device, Interface 11 supplies control signals generated by the controller 13 to the electronic control device. Interface 11 may be a wireless interface such as a network adapter for wireless communication, or it may be a hardware interface for connecting to external devices via cables, etc. Interface 11 may also perform interface operations with various peripheral devices such as input devices, display devices, and sound output devices.

[0029] Memory 12 is composed of various volatile and non-volatile memories such as RAM (Random Access Memory), ROM (Read Only Memory), hard disk drive, and flash memory. Memory 12 stores programs for the controller 13 to execute predetermined processes. Note that the programs executed by the controller 13 may be stored in storage media other than memory 12.

[0030] Furthermore, memory 12 stores the river map database (DB:DataBase) 10 and the highest point information IH.

[0031] The river map DB10 stores feature data, which is data about features (landmarks) that exist on or near rivers. The features mentioned above include, at a minimum, bridges built on rivers that are passable by ships. The feature data includes location information indicating the location where the feature is built, and attribute information representing various attributes of the feature, such as type and size. In addition, the attribute information of feature data corresponding to bridges includes at a minimum information about the height (e.g., elevation) of the bridge's underside (in other words, the bottom surface of the bridge over the river).

[0032] In addition to feature data, the river map DB10 may also include information such as shore locations (including shores and piers) and waterways to which ships can travel. The river map DB10 may also be stored on an external storage device of the information processing device 1, such as a hard disk connected to the information processing device 1 via interface 11. The storage device may be a server device that communicates with the information processing device 1. The storage device may also consist of multiple devices. The river map DB10 may also be updated periodically. In this case, for example, the controller 13 receives partial map information about the area to which its own position belongs from the server device that manages the map information via interface 11 and reflects it in the river map DB10.

[0033] The highest point information IH is information about the height of the highest point (highest point) of the ship in the ship coordinate system, which is a coordinate system based on the ship. For example, the highest point information IH represents the height (distance in the height direction) from the ship's reference position (also called the "ship reference position") to the highest point. In other words, the ship reference position is the origin in the coordinate system used in the point cloud data output by LIDA 3, and corresponds to, for example, the installation position of LIDA 3. The highest point information IH is generated based on prior measurement results and is stored in memory 12 beforehand.

[0034] In addition to the river map DB10, memory 12 also stores information necessary for the processing performed by the information processing device 1 in this embodiment. For example, memory 12 stores information used to set the downsampling size when downsampling is performed on point cloud data obtained when the lidar 3 performs one cycle of scanning. In another example, memory 12 stores information about candidate routes for the navigation route that a ship should take.

[0035] The controller 13 includes one or more processors such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and a TPU (Tensor Processing Unit), and controls the entire information processing device 1. In this case, the controller 13 performs processing related to flight support, etc., by executing programs stored in memory 12, etc.

[0036] Furthermore, the controller 13 functionally includes a candidate route acquisition unit 15, a candidate route suitability determination unit 16, and an operation route determination unit 17.

[0037] The candidate route acquisition unit 15 acquires one or more routes (also called "candidate routes") that are candidates for the ship's operating route. In this case, the candidate route acquisition unit 15 may generate candidate routes based on a route search process, or it may acquire information representing the determined candidate routes from the memory 12 or from other devices via the interface 11. When generating candidate routes, for example, the candidate route acquisition unit 15 searches for candidate routes along the river from the departure point to the destination based on a pair of departure point (which may be the current location) and destination specified by input data supplied from the input device via the interface 11. In this case, the candidate route acquisition unit 15 searches for candidate routes by referring to the river map DB 10 based on an arbitrary route search method. Note that the candidate route generation process does not determine whether a ship can pass under bridges that exist on the route. That is, it is not guaranteed that a ship can pass under bridges that exist on each generated candidate route.

[0038] The candidate route suitability determination unit 16, based on the predicted water level information D1 received from the server device 7, the point cloud data generated by the lidar 3, the river map DB 10, and the highest point information IH, determines whether or not a ship can pass under each bridge located on the candidate route acquired by the candidate route acquisition unit 15 (also called "bridge passage feasibility determination"). Details of the bridge passage feasibility determination will be described later.

[0039] The route determination unit 17 determines the route based on the results of the bridge passage feasibility determination by the candidate route suitability determination unit 16. In this case, the route determination unit 17 recognizes candidate routes in which the vessel is determined to be able to pass over all bridges as suitable candidate routes (also called "suitable candidate routes"), and determines the route the vessel should take from the recognized suitable candidate routes. If there are multiple suitable candidate routes, the route determination unit 17 may select the suitable candidate route with the shortest travel time as the route, or it may display the selectable suitable candidate routes on the display unit via the interface 11 (i.e., display a suitable candidate route selection screen on the display unit), and determine the suitable candidate route selected by the input unit from the displayed suitable candidate routes as the route.

[0040] In the first embodiment, the controller 13 functions as a "means for acquiring predicted water level information," a "means for acquiring candidate routes," a "means for calculating predicted water level for bridges," a "means for calculating the highest point of a ship," a "means for determining a decision," a "means for determining a sailing route," and a computer that executes a program.

[0041] Figure 3(B) is a block diagram showing an example of the hardware configuration of a server device. The server device mainly consists of an interface 71, memory 72, and a controller 73. These elements are interconnected via bus lines.

[0042] Interface 71 performs interface operations related to the exchange of data between the server device and external devices. In this embodiment, interface 71 performs the process of transmitting predicted water level information D1 to the information processing device 1 based on the control of controller 73. In this case, interface 71 may be a wireless interface such as a network adapter for wireless communication, or it may be a hardware interface for connecting to external devices by cables, etc. Interface 71 may also perform interface operations with various peripheral devices such as input devices, display devices, and sound output devices.

[0043] Memory 72 is composed of various volatile and non-volatile memories such as RAM, ROM, hard disk drive, and flash memory. Memory 72 stores a program for the controller 73 to execute predetermined processes. The program executed by the controller 73 may be stored in a storage medium other than memory 72.

[0044] Memory 72 also stores the predicted water level DB 70. The predicted water level DB 70 is a database that records the predicted water level at each water level prediction point on the river. In this case, for example, in the predicted water level DB 70, for each water level prediction point, the location information of the water level prediction point and the predicted water level for each time (date and time) determined according to predetermined time intervals are associated. The predicted water level is determined by comprehensively considering past measurement results from water level gauges installed at the water level prediction point, as well as weather and atmospheric pressure up to the time the water level is predicted. The predicted water level DB 70 may also be stored in an external storage device of the server device 7, such as a hard disk connected to the server device 7 via interface 71. The above storage device may be another server device that communicates with the server device 7. The above storage device may also consist of multiple devices. Furthermore, the predicted water level DB 70 may be updated periodically.

[0045] The controller 73 includes one or more processors such as a CPU, GPU, and TPU, and controls the entire server device 7. In this case, the controller 73 performs processing related to the distribution of predicted water level information D1 by executing programs stored in memory 72, etc.

[0046] (1-3) Candidate route suitability determination process Next, the process for determining suitable candidate routes (also called the "candidate route suitability determination process") will be explained. The candidate route suitability determination unit 16 calculates a predicted water level under each bridge on the candidate route (also called the "bridge predicted water level") according to the scheduled time of passage of the vessel. Then, the candidate route suitability determination unit 16 determines whether or not a vessel can pass under each bridge based on the calculated bridge predicted water level, and determines whether or not the candidate route is a suitable candidate route based on the determination result.

[0047] Figure 4(A) is a map that clearly shows the departure and destination points of a ship and the bridges located on the river. Figure 4(B) is a map that further shows candidate route 91, which has been determined to be a suitable candidate route. In the examples of Figures 4(A) and 4(B), there are bridges along the route from the departure point to the destination, and there are multiple candidate routes due to river branching and merging, etc. On the other hand, it is not guaranteed that the target ship can pass over all the bridges on the map. Therefore, the candidate route suitability determination unit 16 calculates the predicted water level under each bridge on the candidate route according to the ship's planned passage time, and determines whether the ship can pass under each bridge based on the calculated predicted water level. In the examples of Figures 4(A) and 4(B), the candidate route suitability determination unit 16 determines that it is possible for the ship to pass under each of the four bridges on candidate route 91 based on the predicted water level, and determines that candidate route 91 is a suitable candidate route.

[0048] If multiple candidate routes exist, the candidate route suitability determination unit 16 determines whether a vessel can pass under each bridge on each candidate route. The following will explain how to calculate the predicted water level under bridges, and then how to determine whether a bridge can be passed using the predicted water level.

[0049] (1-3-1) Calculation of predicted water level for bridges Figure 5 is an overhead view showing the area around bridges B1 and B2 located on a candidate route. Here, point "Pa1" is the water level prediction point closest to bridges B1 and B2 in the downstream direction, and point "Pa2" is the water level prediction point closest to bridges B1 and B2 in the upstream direction. Point "Pb1" is the point located directly below bridge B1 on the target candidate route, and point "Pb2" is the point located directly below bridge B2 on the target candidate route. Here, points Pb1 and Pb2 correspond to the points where a ship would pass under the bridges when passing through the illustrated river (also called "bridge passing points").

[0050] In this case, the candidate route suitability determination unit 16 identifies the predicted water levels in time series at water level prediction points Pa1 and Pa2, which are closest to bridges B1 and B2 in the upstream and downstream directions, respectively, based on the predicted water level information D1. Figure 6(A) shows graph "Ga1" representing the change in predicted water level at water level prediction point Pa1 and graph "Ga2" representing the change in predicted water level at water level prediction point Pa2. As shown in Figure 6(A), the water level differs depending on the location in the river, and the phase of the change in water level over time differs depending on the location in the river.

[0051] Next, the candidate route suitability determination unit 16 calculates the predicted bridge water levels in time series at bridge passing point Pb1 corresponding to bridge B1 and bridge passing point Pb2 corresponding to bridge B2, based on the predicted water levels in time series at water level prediction point Pa1 and water level prediction point Pa2.

[0052] In this case, for example, the candidate route suitability determination unit 16 calculates the predicted water level of the bridge at bridge passage point Pb1 by linear interpolation for each predicted time period, based on the distance between water level prediction point Pa1 and bridge passage point Pb1, the distance between water level prediction point Pa2 and bridge passage point Pb1, and the predicted water levels at water level prediction point Pa1 and water level prediction point Pa2. Similarly, the candidate route suitability determination unit 16 calculates the predicted water level of the bridge at bridge passage point Pb2 by interpolation (for example, linear interpolation) for each predicted time period, based on the distance between water level prediction point Pa1 and bridge passage point Pb2, the distance between water level prediction point Pa2 and bridge passage point Pb2, and the predicted water levels at water level prediction point Pa1 and water level prediction point Pa2. Figure 6(B) shows graph "Gb1" representing the change in predicted bridge water level at bridge passing point Pb1, and graph "Gb2" representing the change in predicted bridge water level at bridge passing point Pb2, along with graphs Ga1 and Ga2. As shown in Figure 6(B), graph Gb1, which represents the predicted bridge water level at bridge passing point Pb1, which is closer to water level prediction point Pa1 than water level prediction point Pa2, is a graph that is more similar to graph Ga1 than graph Ga2. Similarly, graph Gb2, which represents the predicted bridge water level at bridge passing point Pb2, which is closer to water level prediction point Pa2 than water level prediction point Pa1, is a graph that is more similar to graph Ga2 than graph Ga1.

[0053] The candidate route suitability determination unit 16 may calculate the predicted water level at each bridge passage point using any method (for example, spline interpolation or polynomial approximation) in addition to linear interpolation. In this case, the candidate route suitability determination unit 16 may calculate the predicted water level at the target bridge passage point based on the predicted water levels at three or more nearby water level prediction points for the target bridge passage point.

[0054] Next, the candidate route suitability determination unit 16 determines the scheduled time of passage, which is the time when the vessel is expected to pass each bridge point if it is operated according to the candidate route, and recognizes the predicted water level of the bridge at the scheduled time of passage for each bridge point. In this case, the candidate route suitability determination unit 16 predicts the time when the vessel will pass each bridge point if it is operated according to the target candidate route, based on the scheduled departure time. In this case, for example, the candidate route suitability determination unit 16 calculates the scheduled time of passage at each bridge point based on the scheduled departure time, the expected predicted speed of the vessel, and the required operating distance from the departure point to each bridge point. The candidate route suitability determination unit 16 recognizes the current time as the scheduled departure time if the vessel is scheduled to depart immediately, and otherwise recognizes the time specified by the input data supplied from the input device via the interface 11 as the scheduled departure time. The candidate route suitability determination unit 16 then reads the predicted water level at the time of planned passage from the time-series predicted water level of the bridge calculated by interpolation for each bridge passage point.

[0055] As described above, the candidate route suitability determination unit 16 can accurately calculate the predicted water level for each bridge on the candidate route based on the predicted water level information D1.

[0056] (1-3-2) Determination of whether bridge passage is permitted based on predicted water level at the bridge. The candidate route suitability determination unit 16 predicts the vertical distance (also called the "predicted interval") between the vessel and the bridge at the bridge passage point, based on the predicted water level of the bridge at the scheduled time of passage. Based on this predicted interval, it determines whether or not passage is permitted for each bridge. A specific example of this process will be explained with reference to Figure 7.

[0057] Figure 7 is a diagram illustrating the general method for calculating the prediction interval, showing a ship observed from the rear, assuming the ship is located at the bridge passage point. In the example in Figure 7, the ship is equipped with two lidars 3, and the ship's reference position is defined as the position at the same height as the lidars 3. The ship also has a projection 33 that marks the highest point of the ship. The bridge 30 has a girder substructure 32 that forms the bottom surface of the structural part located above the river, forming a space 31 under the girder through which the ship can pass. Line L1 indicates the origin position of the height (e.g., elevation) used in bridge prediction water levels, line L2 indicates the water surface position, and line L3 indicates the position at the same height as the ship's reference position. Line L4 indicates the position at the same height as the highest point of the ship, and line L5 indicates the position at the same height as the girder substructure 32 that forms the bottom surface of the bridge 30 above the river. Furthermore, the measured points "m5" to "m8" indicate the measured points of the water surface measured by the lidars 3.

[0058] First, the candidate route suitability determination unit 16 extracts feature data corresponding to the bridge 30 that the ship is scheduled to pass over from the river map DB 10, and by referring to the extracted feature data, it determines the height of the bridge 30 (the height corresponding to arrow A2, also called "bridge height"). Note that bridge height represents the height of the bottom surface of the bridge over the river (i.e., the height of the part under the girder). In addition, the predicted water level of bridge 30 is the height corresponding to arrow A1.

[0059] Furthermore, the candidate route suitability determination unit 16 calculates the height distance from the ship's reference position to the water surface (corresponding to arrow A3, also called the "water surface distance") based on the point cloud data (also called "water surface measurement data") from the lidar 3, which measures the water surface at any time before the navigation route is determined. In this case, the candidate route suitability determination unit 16 extracts the point cloud data below the horizontal plane (i.e., in the direction where the depression angle is positive) from the point cloud data output by the lidar 3 as water surface measurement data. At this time, in order to exclude points that detect locations other than the water surface, such as bridge piers, quays, and other ships, the minimum value of the z coordinate of the point cloud data is found, and water surface measurement data can be obtained by extracting the z coordinate values ​​of each measurement point that are close to that minimum value. In Figure 7, the candidate route suitability determination unit 16 considers the data corresponding to the measurement points "m5" to "m8" on the water surface as water surface measurement data and extracts it from the point cloud data of the lidar 3. The candidate route suitability determination unit 16 then calculates a representative value, such as the average or minimum value of the height coordinate values ​​of the extracted water surface measurement data, as the water surface distance. The candidate route suitability determination unit 16 may perform the water surface distance calculation multiple times before determining the operating route, and the average value of the results of the multiple water surface distance calculations may be used as the water surface distance in subsequent processing. Although Figure 7 only shows the measurement points "m5" to "m8" of the lidar 3 on one side (right side), in reality, point cloud data from the lidar 3 on both sides (right side and left side) are used.

[0060] Next, the candidate route suitability determination unit 16 refers to the highest point information IH from the memory 12 to determine the height range from the ship's reference position to the highest point (see arrow A4). Then, the candidate route suitability determination unit 16 calculates the height of the ship's highest point (corresponding to arrow A5, also called the "ship's highest point height"), which is the height obtained by adding the water surface distance (see arrow A3) and the height range from the ship's reference position to the highest point (see arrow A4) to the predicted bridge water level (see arrow A1).

[0061] The candidate route suitability determination unit 16 then calculates the width obtained by subtracting the highest point of the ship from the bridge height as the predicted interval (see arrow A6).

[0062] Subsequently, the candidate route suitability determination unit 16 determines that a vessel can pass over the bridge if the calculated predicted interval is greater than or equal to a threshold (also called the "predicted interval threshold Th"), and determines that there is a risk that the vessel may not be able to safely pass over the bridge if the predicted interval is less than the predicted interval threshold Th. The predicted interval threshold Th may be set to a fixed value pre-stored in memory 12, etc., or it may be set to a variable value. In the latter case, the candidate route suitability determination unit 16 may determine the predicted interval threshold Th based, for example, on an index (e.g., standard deviation) that represents the variation in the height direction of each data point in the water surface measurement data used to calculate the water surface distance. In this case, the candidate route suitability determination unit 16 takes into account, for example, that when the wave height is large, the vertical movement of the vessel in the height direction will also be large, and the variation in the water surface measurement data will also be large, and sets the predicted interval threshold Th to be larger the larger the standard deviation.

[0063] In this way, the candidate route suitability determination unit 16 can accurately determine whether or not it is possible to pass over each bridge on the candidate route, based on the predicted water level of the bridges and the point cloud data and map data related to the bridges output by the lidar 3.

[0064] The candidate route suitability determination unit 16 preferably stores information in memory 12 or the like that associates the scheduled time of passage, the predicted water level of the bridge, and the predicted interval for each bridge present on the candidate route. Figure 8 shows a table that associates the scheduled time of passage, the predicted water level of the bridge, and the predicted interval for each bridge (bridge B1, bridge B2, bridge B3, ...) present on the candidate route. As shown in Figure 8, the candidate route suitability determination unit 16 calculates the scheduled time of passage, the predicted water level of the bridge, and the predicted interval for all bridges (bridge B1, bridge B2, bridge B3, ...) present on the candidate route, and stores the calculation results in memory 12 or the like. In the above table, each bridge may be represented by location information indicating the location where the bridge is located or by other identifiable information, instead of the names of bridges such as bridge B1, bridge B2, bridge B3, etc.

[0065] (1-4) Functional Blocks Figure 9 shows an example of the functional blocks of the controller 13 in the first embodiment. Functionally, the candidate route suitability determination unit 16 includes a bridge height acquisition unit 61, a bridge predicted water level calculation unit 62, a water surface distance calculation unit 63, a ship's highest point height calculation unit 64, a predicted interval calculation unit 65, and a determination unit 66. In Figure 9, blocks that exchange data are connected by solid lines, but the combination of blocks that exchange data is not limited to this. The same applies to the diagrams of other functional blocks described later.

[0066] First, the candidate route acquisition unit 15 acquires one or more candidate routes and supplies information about the acquired candidate routes to the bridge height acquisition unit 61, the bridge predicted water level calculation unit 62, and the operation route determination unit 17, respectively. The candidate route information supplied by the candidate route acquisition unit 15 to the bridge predicted water level calculation unit 62 includes information necessary to calculate the estimated time of passage over each bridge (for example, information about the departure time).

[0067] The bridge height acquisition unit 61 extracts feature data corresponding to bridges present on the candidate route from the river map DB 10, and acquires the bridge height of each bridge present on the candidate route based on the extracted feature data.

[0068] Furthermore, the bridge water level prediction calculation unit 62 calculates the estimated time of passage and the predicted water level at each bridge passage point corresponding to each bridge on the candidate route, based on the predicted water level information D1 received from the server device 7 via the interface 11 and the candidate route information supplied from the candidate route acquisition unit 15. In addition, the water surface distance calculation unit 63 calculates the water surface distance based on water surface measurement data measured at the water surface (i.e., below the horizontal).

[0069] The ship's highest point height calculation unit 64 calculates the ship's highest point height based on the water surface distance calculated by the water surface distance calculation unit 63, the predicted water level at the bridge passage points corresponding to each bridge on the candidate route, and the height range from the ship's reference position to the highest point as indicated by the highest point information IH.

[0070] The prediction interval calculation unit 65 calculates the prediction interval based on the bridge height acquired by the bridge height acquisition unit 61 and the ship's highest point height calculated by the ship's highest point height calculation unit 64. The determination unit 66 then determines whether each bridge on the candidate route is passable based on the prediction interval of each bridge on the candidate route and the prediction interval threshold Th. In this case, the determination unit 66 may adaptively set the prediction interval threshold Th based on the water surface reflection data acquired by the water surface distance calculation unit 63. The determination unit 66 then determines that a candidate route in which a ship can pass over all bridges present on the candidate route is a suitable candidate route, and supplies information regarding the determined suitable candidate route to the navigation route determination unit 17.

[0071] The route determination unit 17 determines the route based on information about suitable candidate routes supplied from the determination unit 66. In this case, if there is only one suitable candidate route, the route determination unit 17 selects that suitable candidate route as the route. On the other hand, if there are multiple suitable candidate routes, the route determination unit 17 may select the suitable candidate route with the shortest travel time as the route, or it may select one route based on indicators other than travel time (for example, river width or the number of vessels operating). In another example, the route determination unit 17 may display the suitable candidate routes in a selectable format on a display unit electrically connected via the interface 11, and select the suitable candidate route specified by input data entered via the interface 11 from an input device operated by the user as the route. In this case, the route determination unit 17 refers to the river map DB 10, generates display information for a screen that overlays the suitable candidate routes on a map of the area including the departure point and destination, and supplies the generated display information to the display device via the interface 11.

[0072] (1-5) Processing flow Figure 10 is an example of a flowchart for determining the operating route. The controller 13 executes the flowchart process, for example, when it detects user input instructing the determination of the operating route after the ship has started up.

[0073] First, the candidate route acquisition unit 15 acquires candidate routes (step S11). In this case, for example, the candidate route acquisition unit 15 searches for candidate routes by performing route search processing based on the destination and departure point (or current location) entered by an input device operated by the user via the interface 11. In other examples, the candidate route acquisition unit 15 may acquire candidate routes by accepting the specification of candidate routes from the input device via the interface 11, or, if candidate routes are stored in memory 12, it may acquire candidate routes by reading them from memory 12.

[0074] Next, the candidate route suitability determination unit 16 receives predicted water level information D1 from the server device 7, which indicates the predicted water level at the predicted water level point on each candidate route (step S12). Then, based on the planned departure time and the predicted water level information D1 obtained in step S12, the candidate route suitability determination unit 16 calculates the planned time of passage and the predicted water level at each bridge passage point on each candidate route (step S13).

[0075] The candidate route suitability determination unit 16 then obtains the bridge height corresponding to each bridge on each candidate route from the river map DB 10 (step S14). Furthermore, the candidate route suitability determination unit 16 calculates the water surface distance based on the water surface measurement data, which is point cloud data from the lidar 3 measured downwards (i.e., in the direction where the depression angle is positive) (step S15). The candidate route suitability determination unit 16 may use a representative value, such as the average value of multiple water surface distance calculation results obtained by executing step S15 multiple times, as the water surface distance value in subsequent processing. Also, steps S11 to S15 may be executed in any order.

[0076] The candidate route suitability determination unit 16 then calculates the highest point height of the vessel at the bridge passing point for each bridge on each candidate route, based on the predicted water level of the bridge, the water surface distance, and the highest point information IH (step S16). The candidate route suitability determination unit 16 then calculates a predicted interval for each bridge on each candidate route, based on the bridge height and the highest point height of the vessel, and determines whether the vessel can pass under each bridge on each candidate route based on the predicted interval (step S17). The operation route determination unit 17 then selects a suitable candidate route, which is a candidate route that can pass through all bridges present on the route, as the operation route, based on the determination result by the candidate route suitability determination unit 16 in step S17 (step S18).

[0077] (1-6) Variation The following describes suitable modifications of the above-described embodiments. These modifications may be applied in combination to these embodiments.

[0078] The candidate route acquisition unit 15 may generate multiple candidate routes of the same path but with different departure times.

[0079] For example, in step S11 of the flowchart in Figure 10, if there are N routes (N is an integer greater than or equal to 1) from the starting point to the destination, the candidate route acquisition unit 15 generates M routes as candidate routes from each of the N routes, with departure times differing at predetermined time intervals (e.g., 30 minutes). In this case, N × M candidate routes are generated. In this case, in step S13, the candidate route suitability determination unit 16 calculates the expected time of passage and the predicted water level at each bridge for each candidate route. Note that even if the candidate routes differ only in departure time, the expected time of passage and the predicted water level at each bridge will be different. Then, each step other than steps S11 and S13 described above is executed in the same manner as in the embodiment described above.

[0080] According to this modified version, the information processing device 1 can increase the number of candidate routes and determine a more suitable flight route.

[0081] As described above, the controller 13 of the information processing device 1 according to the first embodiment acquires predicted water level information D1 regarding the predicted water level at water level prediction points where the water level is predicted on one or more candidate routes that are candidates for the ship's operating route. Based on the predicted water level information D1, the controller 13 calculates the bridge predicted water level, which is the predicted water level at the time the ship is scheduled to pass over each bridge passing point, which is the point where the ship will pass over a bridge on each of the candidate routes. Based on the bridge predicted water level, the controller 13 determines whether each of the candidate routes is suitable as an operating route. In this way, the information processing device 1 can accurately grasp the water level at the time the ship is scheduled to pass over each of the bridge passing points on the candidate routes and suitably determine whether the candidate routes are suitable as operating routes.

[0082] <Second Example> The information processing device 1 according to the second embodiment measures the water level at the predicted water level points that the vessel will pass through after it has started operating according to the determined operating route, and generates correction information for the predicted water level provided by the server device 7 based on the difference between the measured water level and the predicted water level. As a result, the information processing device 1 accurately determines whether the vessel can pass under any unpassed bridges on the operating route (for example, a final confirmation of whether the vessel can actually pass). Hereafter, the same reference numerals will be used for components that are the same as in the first embodiment, and their descriptions will be omitted as appropriate.

[0083] (2-1) Block configuration Figure 11 is a block diagram of an information processing device 1A according to a second embodiment. As shown in the figure, the information processing device 1A has an interface 11, a memory 12, and a controller 13.

[0084] Memory 12 contains the river map DB 10 and the highest point information IH described in the first embodiment. The river map DB 10 may also contain feature data for any features (landmarks) other than bridges whose height (e.g., elevation) is known. This feature data includes at least information about the location and height (e.g., elevation) of the features. Hereafter, features (including bridges) for which feature data is registered in the river map DB 10 will also be referred to as "registered features".

[0085] Functionally, the controller 13 includes a bridge passage determination unit 16A and a predicted water level correction unit 18A.

[0086] The bridge passage feasibility determination unit 16A determines whether it is possible to pass under bridges located on the determined operating route. Furthermore, if water level correction information is supplied from the predicted water level correction unit 18A (described later), the bridge passage feasibility determination unit 16A corrects the predicted water level at each predicted water level point on the operating route based on the water level correction information, and then performs a determination on whether it is possible for a vessel to pass under each bridge on the operating route based on the corrected predicted water level.

[0087] The predicted water level correction unit 18A generates water level correction information to correct the predicted water level at each water level prediction point indicated by the predicted water level information D1 received from the server device 7, and supplies the generated water level correction information to the bridge passage feasibility determination unit 16A.

[0088] Furthermore, the controller 13 according to the second embodiment functions as a "ship reference elevation calculation means," a "measurement water level calculation means," a "correction information generation means," a "bridge water level calculation means," a "bridge passage feasibility determination means," and a computer that executes programs.

[0089] The information processing device 1A may or may not perform the process for determining the sailing route described in the first embodiment. In the latter case, for example, sailing route information is pre-stored in memory 12 or the like, and the information processing device 1A provides sailing route support based on the sailing route recognized by referring to this sailing route information. In this case, the information processing device 1A may determine the sailing route based on input data specifying the sailing route, which is supplied via interface 11 from an input device operated by the user.

[0090] (2-2) Generation of water level correction information Next, the process of generating water level correction information by the predicted water level correction unit 18A will be explained. In general terms, the predicted water level correction unit 18A calculates the height of the ship's reference position (also called the "ship's reference height") based on data measured by the lidar 3 from the navigation route. Then, the predicted water level correction unit 18A generates water level correction information based on the comparison result between the water level calculated based on the water surface distance and the ship's reference height and the predicted water level based on the predicted water level information D1. The water level calculated based on the water surface distance and the ship's reference height corresponds to the water level calculated based on data measured by the lidar 3 from the registered locations, and will hereafter be called the "measured water level".

[0091] Figure 12 is an overhead view showing the area around bridges B3 and B4 located on the navigation route. Here, point "Pa3" is the water level prediction point closest to bridges B3 and B4 in the downstream direction, and point "Pa4" is the water level prediction point closest to bridges B3 and B4 in the upstream direction. Points "Pb3" and "Pb4" are points where the ship reference elevation and measured water level are calculated (also called "water level measurement points"). The dashed line 70 indicates the navigation route.

[0092] First, the calculation of the ship reference elevation and measured water level at water level measurement point Pb3 will be explained. When a ship is present at point Pb3, the predicted water level correction unit 18A recognizes that a registered feature, bridge B3, exists within the measurement range of the lidar 3, based on the ship's position information from the GPS receiver 5, etc., and the position information of registered features included in the river map DB10. The predicted water level correction unit 18A then extracts data (also called "feature measurement data") measured for the registered feature (in this case, bridge B3) from the point cloud data generated by the lidar 3, and calculates the ship reference elevation based on the extracted feature measurement data.

[0093] Figure 13 shows a view of a vessel from the rear when the registered feature, Bridge B3, is within the measurement range of the LIDA 3. Line L11 indicates the origin position of the height (e.g., elevation) used in bridge water level predictions, line L12 indicates the water surface position, and line L13 indicates the position at the same height as the vessel's reference position. Line L14 indicates the position at the same height as the registered feature (Bridge B3 in this case) registered in the river map DB10. Furthermore, the measured points "m9" to "m12" indicate the measured points of the registered feature (Bridge B3 in this case) as measured by the LIDA 3.

[0094] In this case, the predicted water level correction unit 18A, for example, when the distance between the current position of the vessel and the position of the registered feature (in this case, bridge B3) (more specifically, the position of the registered feature registered in the river map DB10) falls within the maximum measurement distance of the lidar 3, extracts point cloud data above the horizontal plane (i.e., in the direction where the elevation angle is positive) from the point cloud data output by the lidar 3 as feature measurement data. At this time, in order to exclude points that detect locations other than the bridge, such as bridge piers, the maximum value of the z coordinate of the point cloud data is determined, and bridge measurement data can be obtained by extracting the z coordinate values ​​of each measurement point that are close to that maximum value. In Figure 13, the predicted water level correction unit 18A considers the data corresponding to the measurement points m9 to m12 of the lower part of the girder 32 as feature measurement data and extracts it from the point cloud data generated by the lidar 3. The predicted water level correction unit 18A then calculates a representative value, such as the average or minimum value of the height coordinate values ​​of the extracted feature measurement data, as the height distance between the registered feature (in this case, bridge B3) and the ship's reference position (the distance corresponding to arrow A12, also called the "feature distance"). The predicted water level correction unit 18A then identifies the height of the target registered feature registered in the river map DB10 (the height corresponding to arrow A11, also called the "feature height"), and calculates the ship's reference height, which corresponds to arrow A13, by subtracting the feature distance from the identified feature height. Note that if the registered feature is a bridge, the feature height represents the bridge height.

[0095] Next, the predicted water level correction unit 18A calculates the measured water level at water level measurement point Pb3 based on the above-mentioned ship reference height based on the geographical measurement data obtained at water level measurement point Pb3 and the water surface measurement data obtained at water level measurement point Pb3. In this case, the predicted water level correction unit 18A calculates the water surface distance, which is the distance corresponding to arrow A14, based on the water surface measurement data, and calculates the measured water level, which is the height corresponding to arrow A15, by subtracting the water surface distance from the ship reference height.

[0096] The predicted water level correction unit 18A then calculates the ship reference height and measured water level at water level measurement point Pb4 using the same processing procedure as at water level measurement point Pb3.

[0097] Next, the predicted water level correction unit 18A calculates a continuous measured water level (also called the "interpolated measured water level") along the route the vessel has already traveled by performing an arbitrary interpolation process using the measured water levels at water level measurement points that the vessel has already passed (i.e., water level measurement points where the measured water level has already been calculated), including water level measurement points Pb3 and Pb4. In this case, the predicted water level correction unit 18A may calculate the continuous interpolated measured water level along the route the vessel has already traveled by using any interpolation method, such as linear interpolation, spline interpolation, or polynomial approximation. In this case, if model information representing a model of the water level along the route is pre-stored in memory 12 or the like, the predicted water level correction unit 18A may determine the parameters of the model by fitting the measured water level values ​​at the water level measurement points to the model. In this case, the predicted water level correction unit 18A recognizes the interpolated measured water level at any point on the route based on the model to which the determined parameters are applied. Note that the above-mentioned model information may be generated in advance for each river, or it may be generated in advance for each section into which the river is divided. Furthermore, the model information may be part of the river map DB10.

[0098] The predicted water level correction unit 18A then compares the predicted water level at each water level prediction point with the interpolated measured water level and calculates the difference between the predicted water level and the interpolated measured water level (also called the "water level difference value"). Hereafter, for convenience, the water level difference value will be assumed to be the value obtained by subtracting the predicted water level from the measured water level (i.e., "water level difference value = interpolated measured water level - predicted water level"). The predicted water level correction unit 18A then calculates a representative value such as the average value of the water level difference values ​​at the multiple water level prediction points that have been passed, and calculates the calculated average value, etc., as a correction amount for the bridge predicted water level.

[0099] Figure 14(A) shows the time evolution of the predicted water level and the measured water level at the location where the vessel is located. Here, the dashed circles 60a to 60k represent the measured water levels at each water level measurement point passed by the vessel, and the solid circles 68a to 68e represent the predicted water levels at each water level prediction point passed by the vessel. Graph 69 is a graph showing the interpolated measured water level generated by interpolating the measured water levels shown by the dashed circles 60a to 60k. The arrows corresponding to the solid circles 68a to 68e each indicate the width corresponding to the water level difference value.

[0100] As shown in Figure 14(A), the predicted water level correction unit 18A calculates the continuously interpolated measured water level along the traversed route by interpolation, and calculates a water level difference value corresponding to the difference between the interpolated measured water level and the predicted water level at the water level prediction points that have been traversed. In the example in Figure 14(A), the predicted water level correction unit 18A calculates the water level difference values ​​at five points corresponding to the solid lines 68a to 68e.

[0101] Figure 14(B) is an example of a distribution showing the frequency of multiple water level difference values ​​calculated within the immediately preceding predetermined period. The predicted water level correction unit 18A aggregates the water level difference values ​​calculated for each water level prediction point that has been passed, and calculates statistics such as the mean and standard deviation of the water level difference values. The predicted water level correction unit 18A then supplies the calculated mean and standard deviation of the water level difference values ​​to the bridge passage feasibility determination unit 16A as water level correction information necessary for correcting the bridge predicted water level.

[0102] Subsequently, the bridge passage feasibility determination unit 16A corrects the predicted water level at each predicted water level point on the operating route based on the water level correction information received from the predicted water level correction unit 18A, and calculates the scheduled passage time and bridge predicted water level at each bridge passage point on the operating route that the vessel has not yet passed, using the same processing as the bridge predicted water level calculation unit 62 in the first embodiment, based on the corrected predicted water level (i.e., the sum of the predicted water level before correction and the average water level difference). Hereafter, the bridge predicted water level calculated based on the corrected predicted water level will also be called the "bridge corrected water level". Then, the bridge passage feasibility determination unit 16A calculates the vessel's highest point height using the same processing as the vessel's highest point height calculation unit 64, based on the water surface distance, the bridge corrected water level, and the highest point information IH, and further calculates the predicted interval from the vessel's highest point height using the same processing as the predicted interval calculation unit 65.

[0103] Preferably, the bridge passage feasibility determination unit 16A stores in memory 12 or the like information relating the calculated scheduled passage time, bridge correction water level, and predicted interval for each bridge on the sailing route that the ship has not yet passed over. Figure 15 shows a table relating the scheduled passage time, bridge correction water level, and predicted interval for each bridge (bridge B5, bridge B6, bridge B7, ...) on the sailing route that the ship has not yet passed over. As shown in Figure 15, the candidate route suitability determination unit 16 calculates the above-mentioned scheduled passage time, bridge correction water level, and predicted interval for all bridges (bridge B5, bridge B6, bridge B7, ...) on the sailing route that the ship has not yet passed over, and stores the calculation results in memory 12 or the like.

[0104] (2-3) Functional Blocks Figure 16 shows an example of the functional blocks of the Predicted Water Level Correction Unit 18A for generating water level correction information in the second embodiment. Functionally, the Predicted Water Level Correction Unit 18A includes a Ground Height Acquisition Unit 81, a Ground Distance Calculation Unit 82, a Ship Reference Height Calculation Unit 83, a Water Surface Distance Calculation Unit 84, a Measured Water Level Calculation Unit 85, and a Water Level Correction Information Generation Unit 86.

[0105] The feature elevation acquisition unit 81 acquires feature information corresponding to registered features located within a predetermined distance from the vessel from the river map DB 10, and acquires the feature elevation included in the feature information. In this case, the feature elevation acquisition unit 81 identifies the above-mentioned registered features based, for example, the current location information based on the GPS receiver 5 and the location information included in the feature information.

[0106] The feature distance calculation unit 82 extracts feature measurement data corresponding to the registered features mentioned above from the point cloud data output by the lidar 3, and calculates the feature distance based on the extracted feature measurement data. The points where feature measurement data is generated correspond to water level measurement points.

[0107] The ship reference elevation calculation unit 83 calculates the ship reference elevation based on the terrain elevation acquired by the terrain elevation acquisition unit 81 and the terrain distance calculated by the terrain distance calculation unit 82.

[0108] The water surface distance calculation unit 84 extracts water surface measurement data measured on the water surface from the point cloud data output by the lidar 3 and calculates the water surface distance based on the extracted water surface measurement data. The measured water level calculation unit 85 calculates the measured water level at the water level measurement point based on the ship reference height calculated by the ship reference height calculation unit 83 and the water surface distance calculated by the water surface distance calculation unit 84. Furthermore, the measured water level calculation unit 85 calculates a continuous interpolated measured water level from the measured water level at previously calculated water level measurement points, from the departure point of the sailing route to the present (more specifically, up to the most recent measured water level calculation point).

[0109] The water level correction information generation unit 86 compares the predicted water level at the predicted water level points already passed by the vessel with the interpolated measured water level, based on the continuous interpolated measured water level calculated by the measured water level calculation unit 85 and the predicted water level information D1, and calculates the water level difference value. The water level correction information generation unit 86 then generates water level correction information, including the average value of the water level difference value at the predicted water level points already passed by the vessel and other statistical quantities, and supplies the generated water level correction information to the bridge passage feasibility determination unit 16A.

[0110] (2-4) Processing flow Figure 17 is an example flowchart showing the procedure for generating water level correction information performed by the predicted water level correction unit 18A in the second embodiment. The predicted water level correction unit 18A repeatedly performs the process shown in the flowchart.

[0111] First, the predicted water level correction unit 18A calculates the feature distance based on feature measurement data extracted from the point cloud data generated by the lidar 3 when the registered feature is within the measurement range of the lidar 3 (step S21). Then, the predicted water level correction unit 18A calculates the ship reference height based on the feature distance calculated in step S21 and the feature height based on the river map DB10 (step S22).

[0112] Then, the predicted water level correction unit 18A calculates the measured water level based on the ship's reference height and the water surface distance calculated from the water surface measurement data extracted from the point cloud data generated by the lidar 3 (step S23). In this case, the predicted water level correction unit 18A calculates at least the interpolated measured water level at the water level prediction point by interpolation processing, etc.

[0113] Then, the predicted water level correction unit 18A generates water level correction information based on the comparison result between the predicted water level at the water level prediction point where the ship has passed and the interpolated measured water level at the said water level prediction point (step S24).

[0114] As described above, the controller 13 of the information processing device 1A according to the second embodiment calculates the ship's reference height, which is the height of the ship's reference position, based on the measurement data of features measured by the lidar 3 installed on the ship. The controller 13 then calculates the measured water level, which represents the height of the water surface, based on the water surface measurement data, which is the measurement data of the water surface measured by the lidar 3, and the ship's reference height. The controller 13 then generates water level correction information related to the predicted water level information, based on the predicted water level information, which represents the predicted water level for each water level prediction point in the river, and the measured water level calculated at the point closest to the water level prediction point corresponding to the said predicted water level. Therefore, by using the generated water level correction value, it becomes possible to accurately estimate the water level at bridge points on the candidate route. As a result, the information processing device 1A generates water level correction information that corrects the predicted water level with high accuracy, even in an embodiment that does not perform high-precision self-position estimation in the height direction, and can accurately re-determine whether a ship can pass under unpassed bridges on the water level prediction navigation route.

[0115] <Third Example> The information processing device 1 according to the third embodiment differs from the second embodiment in that it obtains the ship's reference elevation by performing a highly accurate self-position estimation process that includes the height direction. Hereafter, the same reference numerals will be used for components that are the same as those in the second embodiment, and their descriptions will be omitted.

[0116] (3-1) Block configuration Figure 18 is a block diagram of an information processing device 1B according to the third embodiment. As shown in the figure, the information processing device 1B has an interface 11, a memory 12, and a controller 13.

[0117] Interface 11 acquires output data from each sensor in sensor group 2 and supplies it to controller 13. Sensor group 2 includes a rider 3, a speed sensor 4 for detecting the speed of the vessel, a GPS receiver 5, and an inertial measurement unit (IMU) 6 for measuring the acceleration and angular velocity of the target moving vessel in three axes. The speed sensor 4 may be, for example, a speedometer using Doppler or a speedometer using GNSS.

[0118] Memory 12 contains the river map DB10 and the highest point information IH described in the first embodiment. The river map DB10 contains voxel data VD.

[0119] Voxel data VD is data that records positional information of stationary structures for each voxel, which represents a cube (normal grid), the smallest unit of three-dimensional space. Voxel data VD includes data that represents the measured point cloud data of stationary structures within each voxel according to a normal distribution, and is used for scan matching using NDT (Normal Distributions Transform), as described later. Information processing device 1B estimates, for example, the position on the plane, height position, yaw angle, pitch angle, and roll angle of a ship using NDT scan matching. Unless otherwise specified, the self-position is assumed to include attitude angles such as the yaw angle of the ship. Note that voxel data VD may also be part of the river map DB10.

[0120] Functionally, the controller 13 includes a bridge passage feasibility determination unit 16B, a predicted water level correction unit 18B, and a self-position estimation unit 19B.

[0121] The bridge passage feasibility determination unit 16B determines whether passage is permitted under bridges located on the determined route. The processing performed by the bridge passage feasibility determination unit 16B is the same as that performed by the bridge passage feasibility determination unit 16A in the second embodiment. The predicted water level correction unit 18B generates water level correction information to correct the predicted water level at each predicted water level point indicated by the predicted water level information D1 received from the server device 7, and supplies the generated water level correction information to the bridge passage feasibility determination unit 16B.

[0122] The self-position estimation unit 19B estimates its own position by performing NDT-based scan matching (NDT scan matching) based on point cloud data based on the output of the lidar 3 and voxel data VD corresponding to the voxel to which the point cloud data belongs. Here, the point cloud data to be processed by the self-position estimation unit 19B may be point cloud data generated by the lidar 3, or it may be point cloud data after downsampling the point cloud data. The controller 13 according to the third embodiment functions as a "ship reference elevation calculation means," a "measurement water level calculation means," a "correction information generation means," a "bridge water level calculation means," a "bridge passage feasibility determination means," and a computer that executes programs.

[0123] (3-2) NDT Scan Matching Next, we will explain the position estimation based on NDT scan matching performed by the self-position estimation unit 19B.

[0124] Figure 19 shows the position of a ship in three-dimensional Cartesian coordinates. As shown in the figure, the ship's position on a plane defined in three-dimensional Cartesian coordinates (x, y, z) is represented by the coordinates "(x, y, z)", the ship's roll angle "φ", pitch angle "θ", and yaw angle (azimuth) "ψ". Here, the roll angle φ is defined as the rotation angle around the ship's direction of travel, the pitch angle θ is the elevation angle of the ship's direction of travel relative to the xy plane, and the yaw angle ψ is defined as the angle between the ship's direction of travel and the x-axis. The coordinates (x, y, z) are, for example, an absolute position corresponding to a combination of latitude, longitude, and altitude, or world coordinates indicating a position with a predetermined point as the origin. The self-position estimation unit 19B then performs self-position estimation using these x, y, z, φ, θ, and ψ as estimation parameters.

[0125] Next, we will explain the voxel data VD used for NDT scan matching. The voxel data VD includes data representing the measured point cloud data of stationary structures within each voxel according to a normal distribution.

[0126] Figure 20 shows an example of the schematic data structure of voxel data VD. Voxel data VD contains parameter information for representing the point cloud within the voxels using a normal distribution. In this embodiment, each voxel includes a "voxel ID," "voxel coordinates," "attribute information," "mean vector," and "covariance matrix."

[0127] The "Voxel ID" indicates the identification information of each voxel. The "Voxel Coordinates" indicate the absolute three-dimensional coordinates of a reference position, such as the center position of each voxel. Each voxel is a cube that divides space into a grid, and its shape and size are predetermined, so it is possible to identify the space of each voxel using its voxel coordinates. The voxel coordinates may also be used as the voxel ID.

[0128] "Attribute information" indicates information regarding the attributes of the target voxel. For example, in this embodiment, the "attribute information" of the voxel corresponding to a bridge includes information indicating that it is a bridge part. Note that the "attribute information" of the voxel corresponding to the lower part of the bridge girder (i.e., the bottom surface of the structure above the river) may further include information indicating that it is the lower part of the girder.

[0129] "Average vector" and "covariance matrix" indicate the average vector and covariance matrix corresponding to the parameters when expressing the point cloud within the target voxel by a normal distribution. Note that the coordinates of an arbitrary point "i" within an arbitrary voxel "n" are X n (i)=[x n (i), y n (i), z n (i)] T defined as such, and if the number of points in voxel n is "N n ", then the average vector "μ n " and covariance matrix "V n " in voxel n are respectively represented by the following equations (1) and (2).

[0130]

Number

[0131]

Number

[0132] Next, an overview of NDT scan matching using voxel data VD will be described.

[0133] Scan matching by NDT assuming a ship is an estimation parameter P=[t x , t y , t z , t φ , t θ , t ψT This will lead to the estimation of "t x " is the amount of movement in the x direction, "t y " is the amount of movement in the y direction, "t z " is the amount of movement in the z direction, "t φ " is the roll angle, "t θ " is the pitch angle, "t ψ The symbol " indicates the yaw angle.

[0134] Also, the coordinates of the point cloud data output by the LIDA3 are X L (j) = [x n (j), y n (j), z n (j)] T Therefore, X L (j) Average value "L' n This can be expressed by the following equation (3).

[0135]

number

[0136] The self-position estimation unit 19B then searches for voxel data VD that can be associated with the point cloud data converted to the world coordinate system. Here, the world coordinate system is the absolute coordinate system used in the map DB10 (including the voxel data VD). At this time, the self-position estimation unit 19B may exclude voxel data VD of voxels located below the water surface (in the height direction) from the search target. This allows the information processing device 1B to omit unnecessary processing when associating point cloud data with voxels, thereby suppressing a decrease in position estimation accuracy caused by errors in association.

[0137] Then, the self-localization unit 19B calculates the mean vector μ included in the explored voxel data VD. n and the covariance matrix Vn Using this, the evaluation function value for matching voxel n (also called the "individual evaluation function value") is obtained. n Calculate ".

[0138] In this case, the self-localization unit 19B calculates the individual evaluation function value E of voxel n based on the following equation (4). n Calculate.

[0139]

number

[0140] The self-localization unit 19B then calculates an overall evaluation function value (also called the "score value") "E(k)" for all voxels subject to matching, as shown by equation (5) below. The score value E serves as an indicator of the goodness of fit of the matching.

[0141]

number

[0142]

number

[0143] Figure 21 is an example of a functional block diagram of the self-position estimation unit 19B. As shown in the figure, the self-position estimation unit 19B includes a dead reckoning unit 91, a coordinate transformation unit 92, a water surface reflection data removal unit 93, and an NDT position calculation unit 94.

[0144] The dead reckoning unit 91 calculates the DR position based on the signals output by the sensor group 2. Specifically, the dead reckoning unit 91 uses the ship's moving speed and angular velocity based on the outputs of the speed sensor 4 and IMU 6, etc., to determine the distance traveled and the change in direction from the previous time. Then, the dead reckoning unit 91 calculates the estimated self-position X of time k-1, which is the previous processing time, relative to the current processing time k. ^ For (k-1), the DR position X at time k is obtained by adding the distance traveled and the change in direction from the previous time. DR Calculate (k). This DR position X DR (k) is the self-position at time k based on dead reckoning, and the predicted self-position X - This corresponds to (k). Note that this is immediately after the start of self-localization, and the estimated self-localization X at time k-1. ^ If (k-1) does not exist, the dead reckoning unit 91 determines the DR position X based on the signal output by the GPS receiver 5, for example. DR Determine (k).

[0145] The coordinate transformation unit 92 transforms the point cloud data based on the output of the lidar 3 into the world coordinate system, which is the same coordinate system as the map DB 10. In this case, the coordinate transformation unit 92 performs a coordinate transformation of the point cloud data at time k, for example, based on the predicted self-position output by the dead reckoning unit 91 at time k. Processes for transforming point cloud data in a coordinate system based on a lidar installed on a moving object (a ship in this embodiment) into the coordinate system of the moving object, and processes for transforming from the coordinate system of the moving object into the world coordinate system, are disclosed, for example, in International Publication WO2019 / 188745.

[0146] The water surface reflection data removal unit 93 removes data (also called "water surface reflection data") that was mistakenly generated when the lidar 3 receives light reflected from the water surface, from the point cloud data supplied by the coordinate transformation unit 92. In this case, the water surface reflection data removal unit 93 removes data representing positions below the water surface position (including the same height, the same applies hereinafter) (i.e., positions with the same or lower z-coordinate values) from the point cloud data as water surface reflection data. For example, the water surface reflection data removal unit 93 may estimate the water surface position based on the z-coordinate value after coordinate transformation processing of the point cloud data output by the lidar 3 when the ship is located at a position more than a predetermined distance from the shore. The water surface reflection data removal unit 93 then supplies the point cloud data, from which data below the water surface position has been removed, to the NDT position calculation unit 94.

[0147] The NDT position calculation unit 94 calculates the NDT position based on the point cloud data supplied from the water surface reflection data removal unit 93. In this case, the NDT position calculation unit 94 matches the point cloud data in the world coordinate system supplied from the water surface reflection data removal unit 93 with the voxel data VD represented in the same world coordinate system. Then, for each voxel that has been matched with the point cloud data, the NDT position calculation unit 94 calculates an individual evaluation function value based on equation (4) and calculates the estimated parameter P that maximizes the score value E(k) based on equation (5). Then, based on equation (6), the NDT position calculation unit 94 calculates the DR position X output by the dead reckoning unit 91. DR The NDT position X at time k is determined by applying the estimated parameter P obtained at time k to (k). NDT (k) is determined. The NDT position calculation unit 94 calculates the NDT position X NDT Output (k) as the estimated self-position X^(k) at time k.

[0148] (3-3) Generation of water level correction information The predicted water level correction unit 18B calculates the measured water level based on the ship's reference height obtained by self-position estimation and the water surface distance based on water surface measurement data, and generates water level correction information based on the comparison result between the measured water level and the predicted water level.

[0149] Figure 22 is a view of the vessel from the rear. Line L21 indicates the origin position of the height (e.g., elevation) used in bridge prediction water levels, line L22 indicates the water surface position, and line L23 indicates the position that is at the same height as the vessel's reference position. Furthermore, the measured points "m13" to "m16" indicate the measured points on the water surface measured by the lidar 3. In this case, the predicted water level correction unit 18B calculates the water surface distance, which is the distance corresponding to arrow A22, by performing the same processing as the candidate route suitability determination unit 16 in the first embodiment, based on the water surface measurement data corresponding to the measured points "m13" to "m16" on the water surface. Then, the predicted water level correction unit 18B calculates the measured water level by subtracting the water surface distance of arrow A22 from the vessel's reference height, which is the height corresponding to arrow 21 obtained by self-position estimation (i.e., the height indicated by arrow A23).

[0150] Figure 23(A) shows the time evolution of the predicted water level and the measured water level at the location where the vessel is located. Here, graph 69B shows the measured water level calculated from the self-position estimation result in the height direction (z coordinate) and the water surface distance based on water surface measurement data. As mentioned above, it is possible to calculate the measured water level from the self-position estimation and water surface distance each time LIDA3 detects point cloud data. Therefore, although the measured water level is discrete, the period of LIDA3 is short (e.g., 100 [ms]), so graph 69B is close to a continuous line. The solid circles 68a to 68e represent the predicted water level at the water level prediction points that the vessel passed through. The arrows corresponding to the solid circles 68a to 68e each indicate the width corresponding to the water level difference value.

[0151] As shown in the figure, the predicted water level correction unit 18B calculates the measured water level along the navigation route based on the self-position estimation results continuously obtained by the self-position estimation unit 19B, and calculates a water level difference value corresponding to the difference between the measured water level and the predicted water level at each water level prediction point that the vessel has already passed. In the example in Figure 23(A), the predicted water level correction unit 18B calculates the water level difference values ​​at five points corresponding to the solid lines 68a to 68e.

[0152] Figure 23(B) is an example of a distribution representing the frequency of multiple water level difference values ​​calculated within the immediately preceding predetermined period. The predicted water level correction unit 18B aggregates the water level difference values ​​calculated for each water level prediction point that has already been passed, and calculates statistics such as the mean and standard deviation of the water level difference values. The predicted water level correction unit 18B then supplies the calculated mean and standard deviation of the water level difference values ​​to the bridge passage feasibility determination unit 16B as water level correction information necessary for correcting the bridge predicted water level. Subsequently, the bridge passage feasibility determination unit 16B corrects the predicted water level at each water level prediction point on the operating route based on the water level correction information received from the predicted water level correction unit 18B, and calculates the scheduled passage time and bridge correction water level at each bridge passage point on the operating route that the vessel has not yet passed, based on the corrected predicted water level (i.e., the sum of the predicted water level before correction and the average water level difference value). The bridge passage feasibility determination unit 16B then calculates the ship's highest point height based on the water surface distance, the bridge correction water level, and the highest point information IH, and calculates the predicted interval from the ship's highest point height. Preferably, the bridge passage feasibility determination unit 16B stores information (see Figure 15) in memory 12 or the like that associates the calculated planned passage time, the bridge correction water level, and the predicted interval for each bridge on the route that the ship has not yet passed over.

[0153] (3-4) Functional Blocks Figure 24 shows an example of the functional blocks of the predictive water level correction unit 18B related to the generation of water level correction information in the third embodiment. Functionally, the predictive water level correction unit 18B includes a ship reference height acquisition unit 83B, a water surface distance calculation unit 84B, a measured water level calculation unit 85B, and a water level correction information generation unit 86B.

[0154] The ship reference height acquisition unit 83B acquires the z-coordinate value included in the self-position estimation result supplied from the self-position estimation unit 19B as the ship reference height. The water surface distance calculation unit 84B extracts water surface measurement data measured from the water surface from the point cloud data output by the lidar 3 and calculates the water surface distance based on the extracted water surface measurement data. Note that the calculation of the water surface distance may be performed at a lower frequency than the frequency of self-position estimation by the self-position estimation unit 19B.

[0155] The water level measurement unit 85B calculates the water level based on the ship reference height acquired by the ship reference height acquisition unit 83B and the water surface distance calculated by the water surface distance calculation unit 84B. The water level measurement unit 85B may calculate the water level each time a self-position estimation result is obtained, or it may calculate the water level at a frequency lower than the frequency at which a self-position estimation result is obtained.

[0156] The water level correction information generation unit 86B compares the predicted water level at the predicted water level points already passed by the vessel with the measured water level, based on the measured water level calculated by the measured water level calculation unit 85B and the predicted water level information D1, and calculates the water level difference value. The water level correction information generation unit 86B then generates water level correction information, including the average value of the water level difference value at the predicted water level points already passed by the vessel and other statistical quantities, and supplies the generated water level correction information to the bridge passage feasibility determination unit 16B.

[0157] (3-5) Processing flow Figure 25 is an example flowchart showing the procedure for generating water level correction information performed by the predicted water level correction unit 18B in the third embodiment.

[0158] First, the predicted water level correction unit 18B acquires the ship's reference elevation based on the self-position estimation result by the self-position estimation unit 19B (step S31). Then, the predicted water level correction unit 18B calculates the measured water level based on the ship's reference elevation and the water surface distance calculated from the water surface measurement data extracted from the point cloud data generated by the lidar 3 (step S32). The predicted water level correction unit 18B repeatedly performs the processes in steps S31 and S32 until the ship passes the predicted water level point.

[0159] Then, when a vessel passes a water level prediction point, the predicted water level correction unit 18B generates water level correction information based on the comparison result between the predicted water level at the water level prediction point and the measured water level (step S33). In this case, the predicted water level correction unit 18B generates water level correction information based on multiple water level difference values ​​(i.e., the water level difference value at the water level prediction point immediately after passing it, and the water level difference value at water level prediction points passed before the said water level prediction point).

[0160] (3-6) Variation The voxel data VD is not limited to a data structure that includes a mean vector and a covariance matrix, as shown in Figure 20. For example, the voxel data VD may include the point cloud data used to calculate the mean vector and covariance matrix. Furthermore, the self-localization method using the voxel data VD is not limited to NDT scan matching. For example, the information processing device 1B may perform self-localization by matching the voxel data VD with the point cloud data of LIDA 3 based on ICP (Iterative Closest Point).

[0161] As described above, the controller 13 of the information processing device 1B according to the third embodiment calculates the ship's reference height, which is the height of the ship's reference position, based on the self-position estimation result using the measurement data of features measured by the lidar 3 installed on the ship. The controller 13 then calculates the measured water level, which represents the height of the water surface, based on the water surface measurement data, which is the measurement data of the water surface measured by the lidar 3, and the ship's reference height. The controller 13 then generates water level correction information related to the predicted water level information, based on the predicted water level represented by the predicted water level information, which represents the predicted water level for each water level prediction point in the river, and the measured water level calculated at the water level prediction point corresponding to the predicted water level or the point closest to the water level prediction point. Therefore, by using the generated water level correction value, it becomes possible to accurately estimate the water level at bridge points on the candidate route. As a result, in an embodiment where the information processing device 1B performs high-precision self-position estimation in the height direction, it generates water level correction information that corrects the predicted water level with high precision using the self-position estimation results, and can accurately determine whether or not a vessel can pass under bridges that have not yet been crossed on the predicted water level navigation route.

[0162] <Fourth Example> In the fourth embodiment, the server device 7 collects the water level difference value calculated based on the second or third embodiment, and updates the predicted water level DB 70 based on the collected water level difference value. As a result, the server device 7 preferably distributes predicted water level information D1, which shows a highly accurate water level corrected based on the measured water level, to the information processing device 1.

[0163] (4-1) composition Figure 26 shows a schematic configuration of the operation support system according to the fourth embodiment. The operation support system according to the fourth embodiment has multiple information processing devices 1C (1Ca, 1Cb, ...) that perform processing according to either the second or third embodiment. The server device 7C according to the fourth embodiment receives measured water level information "D2" related to the correction of the predicted water level based on the measured water level from each of the information processing devices 1C, and updates the predicted water level DB based on the received measured water level information D2. Hereafter, components that are the same as those in the first, second, or third embodiment will be denoted by the same reference numerals as appropriate, and their descriptions will be omitted.

[0164] The information processing device 1C according to the fourth embodiment has the same hardware configuration as the information processing device 1A according to the second embodiment shown in Figure 11 or the information processing device 1B according to the third embodiment shown in Figure 18. The information processing device 1C moves together with the vessel present on the river and provides operational support for the vessel equipped with the information processing device 1 based on the outputs of various sensors included in the sensor group 2 (not shown) and the predicted water level information D1 transmitted from the server device 7C. When high-precision self-position estimation including the height direction is not performed, the information processing device 1C calculates the water level difference value based on the second embodiment, and when high-precision self-position estimation including the height direction is performed (for example, self-position estimation based on NDT scan matching), it calculates the water level difference value based on the third embodiment. The information processing device 1C then transmits measured water level information D2, which includes the calculated water level difference value, location information representing the place where the water level difference value was calculated (i.e., the water level prediction point), and the time (date and time) when the water level difference value was calculated, to the server device 7C.

[0165] The server device 7C according to the fourth embodiment updates the predicted water level DB 70 based on the measured water level information D2 received from the information processing device 1C. Figure 27 is a block diagram showing an example of the hardware configuration of the server device 7C. The server device 7C mainly comprises an interface 71, a memory 72, and a controller 73. Each of these elements is interconnected via a bus line.

[0166] Interface 71 performs interface operations related to the exchange of data between the server device 7C and external devices. In this embodiment, based on the control of the controller 73, interface 71 performs the process of transmitting predicted water level information D1 to the information processing device 1C and the process of receiving measured water level information D2 from the information processing device 1C. Memory 72 is composed of various volatile and non-volatile memories such as RAM, ROM, hard disk drive, and flash memory. Memory 72 stores a program for the controller 73 to execute predetermined processes. Memory 72 also stores the predicted water level DB 70 described in the first embodiment and the measured water level information DB 75, which is a database that stores the measured water level information D2. The predicted water level DB 70 is updated based on the measured water level information DB 75, as will be described later.

[0167] (4-2) data structure Figure 28(A) shows an example of the data structure of the predicted water level DB70. The predicted water level DB70 mainly contains the following items: "Predicted water level location," "Update time," and "Predicted water level."

[0168] "Water level prediction point" is information that identifies the target water level prediction point in each record of the predicted water level DB70. Here, as an example, the 2D coordinate values ​​(e.g., a pair of latitude and longitude) of the water level prediction point are recorded as "Water level prediction point". "Update date and time" indicates the date and time when the predicted water level was updated. If the predicted water level has not been updated, "Update date and time" indicates the date and time of the prediction of the predicted water level. "Update date and time" may be set for each predicted water level for each time period. "Predicted water level" indicates the predicted water level for each time period when the water level is predicted. Here, the value of each predicted water level in "Predicted water level" is updated based on the measured water level information DB75.

[0169] Figure 28(B) shows an example of the data structure of the measured water level information D2. The measured water level information D2 mainly consists of the following items: "location information", "date and time", "water level difference value", "water level difference standard deviation", "predicted water level", and "measured water level".

[0170] "Location information" refers to the location where the water level difference value was calculated (i.e., the water level prediction point, or a location close to the water level prediction point). Here, "location information" is represented by two-dimensional coordinate values ​​that represent the aforementioned location on the horizontal plane. "Date and time" refers to the date and time (time) when the water level difference value (or measured water level) was calculated. "Water level difference value" refers to the average value of the water level difference. "Standard deviation of water level difference" refers to the standard deviation of the water level difference. "Predicted water level" refers to the predicted water level used to calculate the corresponding water level difference value. Note that "predicted water level" may include, in addition to or instead of the predicted water level, information regarding the date and time (update date and time) when the predicted water level was predicted or updated. This time information refers to, for example, the update date and time shown in Figure 28(A) if the predicted water level information D1 received by the information processing device 1C includes such update date and time information. "Measured water level" refers to the measured water level used to calculate the corresponding water level difference value.

[0171] Note that the data structure of the measured water level information D2 is not limited to the data structure shown in the diagram. For example, the measured water level information D2 does not have to include information on "predicted water level" and "measured water level". In another example, the measured water level information D2 may contain only one record instead of multiple records. In this case, the information processing device 1C sends the measured water level information D2, which includes a record related to the calculated water level difference value, to the server device 7C each time it calculates the water level difference value.

[0172] (4-3) Predicted water level update process Next, we will specifically explain the process of updating the predicted water level recorded in the predicted water level DB70 (predicted water level update process).

[0173] Figure 29 shows an example of the functional block of the controller 73 of the server device 7C according to the fourth embodiment. As shown in the figure, the controller 73 of the server device 7C according to the fourth embodiment functionally includes a receiving unit 76, an update unit 77, and a distribution unit 78.

[0174] The receiving unit 76 receives measured water level information D2 from each information processing device 1C via the interface 71. The receiving unit 76 then stores the received measured water level information D2 in the measured water level information DB 75.

[0175] The update unit 77 updates the predicted water level DB 70 based on the measured water level information DB 75. In this case, for example, the update unit 77 first identifies the predicted water level point indicated by the location information (i.e., the water level point closest to the point indicated by the location information) for each record recorded in the measured water level information DB 75 (i.e., the record of measured water level information D2 shown in Figure 28). Then, the update unit 77 aggregates the water level difference values ​​included in the records of the measured water level information DB 75 for each predicted water level point, and corrects the predicted water level of the predicted water level point recorded in the predicted water level DB 70 based on a representative value such as the average value of the water level difference values ​​for each predicted water level point. In this case, for example, the update unit 77 performs statistical processing on the water level difference values ​​received from multiple information processing devices for each predicted water level point, and adds a representative value such as the average value to the predicted water level of the corresponding predicted water level point at each time.

[0176] Furthermore, when performing statistical processing on the water level difference values ​​received from the multiple information processing devices described above, the update unit 77 may perform weighted averaging using the standard deviation of the water level difference. For example, the water level difference values ​​μ from N information processing devices 1C i and water level difference standard deviation σ i When a signal is received (i=1,2,···,N), the update unit 77 calculates a correction value "Cw" for the predicted water level using the following equation (7).

[0177]

number

[0178] In this case, the update unit 77 preferably corrects the predicted water level based on a water level difference value calculated after the update time corresponding to the predicted water level recorded in the predicted water level DB 70. For example, when the update unit 77 updates the first record in Figure 28(A), it extracts a record from the measured water level information DB 75 in which the "location information" indicates a location within a predetermined distance (a threshold for determining whether it is the same location or not) from the water level prediction point "(va,wa)" and the "date and time" is "September 12, 17:00" or later, and corrects the predicted water level based on the water level difference value indicated by the extracted record. Note that if the measured water level information DB 75 contains information indicating the update date and time of the predicted water level used to calculate the water level difference value, the update unit 77 may correct the predicted water level using only water level difference values ​​in which this update date and time matches the update date and time recorded in the predicted water level DB 70. This preferably suppresses the correction of the predicted water level using water level difference values ​​calculated based on older predicted water levels.

[0179] The distribution unit 78 transmits predicted water level information D1, which includes information on predicted water levels extracted from the predicted water level DB 70, to the information processing device 1C via the interface 71. In this case, when the distribution unit 78 receives predetermined request information from the information processing device 1C, it may transmit predicted water level information D1 relating to the predicted water levels of the area or water level prediction points within the river specified by the request information to the information processing device 1C, or it may transmit predicted water level information D1 relating to the updated predicted water levels to the information processing device 1C each time the predicted water level DB 70 is updated. In this way, the distribution unit 78 distributes information on predicted water levels to the information processing device 1C based on either a push-type information distribution or a pull-type information distribution method.

[0180] Figure 30(A) is an example of a flowchart executed by the information processing device 1C according to the fourth embodiment. The information processing device 1C repeatedly executes the processing shown in this flowchart while the ship is in operation.

[0181] The information processing device 1C receives predicted water level information D1 from the server device 7C and stores the received predicted water level information D1 (step S41). Next, the information processing device 1C calculates the measured water level and water level difference value at each water level prediction point that the vessel has passed through, according to the second or third embodiment (step S42). In this case, the information processing device 1C obtains the measured water level and water level difference value at each water level prediction point by executing the water level correction information generation process flowchart shown in Figure 17 or Figure 25. Then, the information processing device 1C determines whether or not it is time to send the measured water level information D2 (step S43). For example, the information processing device 1C may send records corresponding to the calculated water level difference values ​​to the server device 7C as measured water level information D2 each time a predetermined number of water level calculation values ​​are calculated, or it may send records corresponding to all calculated water level difference values ​​to the server device 7C as measured water level information D2 after the completion of the operation of the route.

[0182] Then, if it is the time to transmit the measured water level information D2 (step S43; Yes), the information processing device 1C transmits the measured water level information D2, which includes the water level difference value calculated in step S42 and the corresponding date and time information and location information, to the server device 7C (step S44). On the other hand, if the information processing device 1C determines that it is not the time to transmit the measured water level information D2 (step S43; No), and there is a water level prediction point that the ship has passed through, it continues to calculate the measured water level and water level difference value at that water level prediction point in step S42.

[0183] Figure 30(B) is an example of a flowchart executed by the server device 7C according to the third embodiment. The server device 7C repeatedly executes the processes shown in this flowchart.

[0184] First, the server device 7C receives the measured water level information D2 transmitted from the information processing device 1C and stores the received measured water level information D2 in the measured water level information DB 75 (step S51). Then, the server device 7C determines whether or not it is time to update the predicted water level DB 70 (step S52). For example, the server device 7C may determine that it is time to update the predicted water level for a water level prediction point if there is a water level prediction point from which a predetermined number or more water level difference values ​​have been collected. In another example, the server device 7C may determine that it is time to update the predicted water level DB 70 at predetermined time intervals and update the predicted water level for the water level prediction point from which water level difference values ​​have been collected. If the server device 7C determines that it is not time to update the predicted water level DB 70 (step S52; No), it continues to receive and store the measured water level information D2 in step S51.

[0185] On the other hand, if the server device 7C determines that it is time to update the predicted water level DB 70 (step S52; Yes), it calculates the updated predicted water level value based on the water level difference value registered in the measured water level information DB 75 (step S53). In this case, the server device 7C calculates the updated predicted water level value for each water level prediction point, for example, based on the average value of the water level difference value and the predicted water level before the update. Then, the server device 7C updates the predicted water level DB 70 based on the calculated updated predicted water level value (step S54). After that, the server device 7C distributes the predicted water level information D1 based on the updated predicted water level DB 70 to each information processing device 1C. As a result, the server device 7C can distribute the predicted water level information D1, which accurately reflects the water level measured at each water level prediction point and shows an accurate predicted water level, to the information processing device 1C.

[0186] (4-4) Variation The server device 7C may calculate the updated predicted water level based on the measured water level included in the measured water level information D2, instead of calculating the updated predicted water level based on the difference in water level included in the measured water level information D2.

[0187] In this modified example, the measured water level information D2 includes the measured water level at the water level prediction point and information indicating the date, time, and location where the measured water level was calculated. The server device 7C then calculates the water level difference value by referring to the predicted water level DB 70 and the measured water level information DB 75 which reflects the measured water level information D2, and calculates the updated predicted water level value according to the description of the fourth embodiment above based on the calculated water level difference value. In this case, the server device 7C refers to the measured water level information DB 75 and calculates representative values ​​such as the average value of the measured water level for each water level prediction point and time period (time periods corresponding to "time a" and "time b" in the predicted water level DB 70 shown in Figure 28(A)). The server device 7C then calculates the difference value between the predicted water level registered in the predicted water level DB 70 and the representative value such as the average value of the corresponding measured water level as the water level difference value for each water level prediction point and time period. The server device 7C then calculates representative values ​​such as the average of the calculated water level difference values ​​for each water level prediction point, and calculates updated predicted water levels for each water level prediction point based on the calculated water level difference values.

[0188] In this modified example, the server device 7C can update each predicted water level recorded in the predicted water level DB 70 to an accurate value that accurately reflects the water level measured at each water level prediction point, and distribute the predicted water level information D1, which shows the accurate predicted water level, to the information processing device 1C.

[0189] As described above, the controller 73 of the server device 7C according to the fourth embodiment stores a predicted water level DB 70 representing the predicted water level for each water level prediction point in the river. The controller 73 receives measured water level information D2 from multiple vessels regarding the measured water level measured by each vessel. The controller 73 then updates the predicted water level DB 70 based on the measured water level information D2. The controller 73 then distributes predicted water level information D1 based on the predicted water level DB 70. In this embodiment, the server device 7C can distribute predicted water level information D1, which accurately reflects the water level measured at each water level prediction point and shows an accurate predicted water level, to the information processing device 1C.

[0190] In the embodiments described above, the program can be stored using various types of non-transitory computer-readable media and supplied to a computer, such as a controller. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic storage media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical storage media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / Ws, and semiconductor memory (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, RAMs (Random Access Memory)).

[0191] The present invention has been described above with reference to the examples, but the present invention is not limited to the above examples. Various modifications to the structure and details of the present invention can be made that can be understood by a person skilled in the art within the scope of the present invention. That is, the present invention naturally includes the full disclosure, including the claims, and various modifications and alterations that a person skilled in the art could make in accordance with the technical idea. Furthermore, each disclosure of the above-mentioned patent documents, etc., that has been cited is incorporated herein by reference. [Explanation of symbols]

[0192] 1, 1A, 1B, 1C Information Processing Devices 2 Sensor Groups 3 Riders 5 GPS receivers 7, 7C server device 10 River Map Database

Claims

1. a vessel reference height calculation means for calculating a vessel reference height, which is the height of a reference position of the vessel, based on measurement data of features measured by a measuring device installed on the vessel; a measured water level calculation means for calculating a measured water level representing the height of the water surface based on water surface measurement data, which is measurement data of the water surface measured by the measurement device, and the ship reference height; a correction information generating means for generating correction information for the predicted water level information based on a predicted water level indicated by predicted water level information indicating a predicted water level for each water level prediction point in a river and the measured water level calculated at the water level prediction point corresponding to the predicted water level or at a point closest to the water level prediction point; An information processing device having the above.

2. 2. The information processing device according to claim 1, wherein the ship reference height calculation means estimates the position of the ship based on measurement data of the feature and map data relating to the feature, and obtains the result of the position estimation in the height direction as the ship reference height.

3. 3. The information processing device described in claim 2, wherein the correction information generation means generates the correction information based on the difference value between the measured water level calculated from the ship reference height based on the result of the position estimation at the water level prediction point or the point closest to the water level prediction point and the predicted water level.

4. the map data is voxel data that represents the position of an object for each voxel, which is a unit area; 4. The information processing apparatus according to claim 2, wherein the ship reference height calculation means performs the position estimation based on a comparison between the voxel data and measurement data of the feature.

5. 2. The information processing device according to claim 1, wherein the ship reference height calculation means calculates the ship reference height based on the vertical distance between the feature and the reference position calculated based on measurement data of the feature and the height of the feature based on map data relating to the feature.

6. The information processing device described in claim 5, wherein the correction information generation means calculates consecutive interpolated measured water levels on the ship's navigation route by interpolating the measured water levels calculated at multiple points, and generates the correction information based on the difference value between the interpolated measured water level at the water level prediction point and the predicted water level.

7. An information processing device according to any one of claims 1 to 6, further comprising a bridge water level calculation means for calculating a bridge water level, which is the water level at a bridge point where a bridge is located, based on the correction information and the predicted water level.

8. 8. The information processing device according to claim 7, further comprising a bridge passability determination means for determining whether the vessel can pass under a bridge that the vessel is scheduled to pass under, based on the bridge water level, the water surface distance which is the vertical distance between the reference position based on the water surface measurement data and the water surface, the height from the reference position to the highest point of the vessel, and the height of the bridge based on map data.

9. A computer-implemented control method comprising: Calculating a ship reference height, which is the height of the ship's reference position, based on measurement data of features measured by a measuring device installed on the ship; Calculating a measured water level representing the height of the water surface based on water surface measurement data, which is measurement data of the water surface measured by the measuring device, and the ship reference height; generating correction information for the predicted water level information based on a predicted water level indicated by predicted water level information indicating a predicted water level for each water level prediction point in a river and the measured water level calculated at the water level prediction point corresponding to the predicted water level or at a point closest to the water level prediction point; Control method.

10. Calculating a ship reference height, which is the height of the ship's reference position, based on measurement data of features measured by a measuring device installed on the ship; Calculating a measured water level representing the height of the water surface based on water surface measurement data, which is measurement data of the water surface measured by the measuring device, and the ship reference height; A program that causes a computer to execute a process of generating correction information for the predicted water level information based on the predicted water level represented by predicted water level information that shows the predicted water level for each water level prediction point in a river, and the measured water level calculated at the water level prediction point corresponding to the predicted water level or the point closest to the water level prediction point.

11. A storage medium storing the program according to claim 10.

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