Information processing device, determination method, program, and storage medium

Abstract

To appropriately determine whether routes to be candidates of a navigation route have adequacy as the navigation route.SOLUTION: A controller 13 of an information processing device 1 acquires predicted water level information D1 concerning a predicted water level at a water level prediction point where a water level is predicted on one or more candidate routes to be candidates of a navigation route of a ship. Then, the controller 13 calculates, based on the predicted water level information D1, a bridge predicted water level which is the predicted water level at a passage scheduled time of the ship at each of bridge passage points which are points where the ship passes under a bridge present on each of the candidate routes. Then, the controller 13 determines the presence / absence of adequacy of each of the candidate routes as the navigation route based on the bridge predicted water level.SELECTED DRAWING: Figure 10

Description

[Technical Field]

[0001] The present disclosure relates to determining ship routes. [Background technology]

[0002] Conventionally, there have been known techniques for estimating the self-position of a moving object by comparing (matching) shape data of surrounding objects measured using a measurement device such as a laser scanner 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 obtained by dividing a space according to a predetermined rule is a stationary object or a moving object, and matches the map information with the measurement data for voxels in which a stationary object exists. Furthermore, Patent Document 2 discloses a scan matching method that estimates the self-position by comparing voxel data including 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 automatically docks a ship so that light emitted from the LIDAR is reflected by objects around the docking position and received by the LIDAR. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication WO2013 / 076829 [Patent Document 2] International Publication No. WO2018 / 221453 [Patent Document 3] Japanese Patent Publication No. 2020-59403 Summary of the Invention [Problem to be solved by the invention]

[0004] When deciding on a navigation route, it is necessary to select a route that allows ships to pass bridges safely. In selecting such a route, it is necessary to take into account that water levels vary depending on the time of day and the location on the river, and to accurately determine whether the candidate route is suitable as a navigation route for ships.

[0005] The present disclosure has been made to solve the above-mentioned problems, and one of its main objectives is to provide an information processing device that can accurately determine whether a candidate route for an operation route is suitable as an operation route. [Means for solving the problem]

[0006] The claimed invention is a predicted water level information acquisition means for acquiring predicted water level information relating to predicted water levels at water level prediction points where water levels are predicted on one or more candidate routes that are candidates for the ship's navigation route; a bridge predicted water level calculation means for calculating, based on the predicted water level information, a bridge predicted water level, which is a predicted water level at the scheduled time of passage of the vessel at each bridge passing point, which is a point where the vessel will pass a bridge existing on each of the candidate routes; a determining means for determining whether each of the candidate routes is suitable as the navigation route based on the predicted bridge water level; The information processing device has the following.

[0007] The claimed invention also includes: A computer-implemented determination method comprising: Obtaining predicted water level information regarding predicted water levels at water level prediction points where water levels are predicted on one or more candidate routes that are candidates for the ship's navigation route; Based on the predicted water level information, calculate a bridge predicted water level, which is a predicted water level at each bridge passing point, which is a point where the vessel will pass a bridge that exists on each of the candidate routes, at the scheduled time of passage of the vessel; determining whether each of the candidate routes is suitable as the navigation route based on the predicted bridge water level; This is a judgment method.

[0008] The claimed invention also includes: Obtaining predicted water level information regarding predicted water levels at water level prediction points where water levels are predicted on one or more candidate routes that are candidates for the ship's navigation route; Based on the predicted water level information, calculate a bridge predicted water level, which is a predicted water level at each bridge passing point, which is a point where the vessel will pass a bridge that exists on each of the candidate routes, at the scheduled time of passage of the vessel; This is a program that causes a computer to execute a process of determining whether each of the candidate routes is suitable as the navigation route based on the predicted bridge water level. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a schematic configuration diagram of an operation support system. [Figure 2] FIG. 1 is a diagram illustrating an example of the field of view of a ship and a lidar included in a navigation support system. [Figure 3] 1A is a block diagram showing an example of the hardware configuration of an information processing device, and FIG. 1B is a block diagram showing an example of the hardware configuration of a server device. [Figure 4] This is a map that clearly shows the departure and destination points of ships and bridges on rivers. [Figure 5] This is an overhead view showing the vicinity of a bridge on a candidate route. [Figure 6] (A) A graph showing the change in predicted water level at the water level prediction point. (B) A graph showing the change in predicted water level at the water level prediction point and a graph showing the change in predicted bridge water level at the bridge passing point. [Figure 7] This is a view of a ship observed from behind, assuming that the ship is at a bridge passing point. [Figure 8] This table shows the estimated time of crossing each bridge on the candidate route, the predicted water level at the bridge, and the prediction interval. [Figure 9] 10 is an example of a functional block of a controller related to a flight route determination process. [Figure 10] 10 is an example of a flowchart of a flight route determination process. [Figure 11] FIG. 10 is a block diagram of an information processing device according to a second embodiment. [Figure 12] This is an aerial view showing the area around the bridges on the operating route. [Figure 13] This is a diagram of a ship observed from behind when a registered feature is present within the measurement range of the lidar. [Figure 14] (A) Shows the time change between the predicted water level and the measured water level at the location where the ship is located. (B) An example of the frequency distribution of multiple water level difference values ​​calculated within the immediately preceding specified period. [Figure 15] This table shows the estimated time of passage, bridge corrected water level, and prediction interval for each bridge on the navigation route that the ship has not yet passed. [Figure 16] 10 is an example of a functional block of a predicted water level correction unit related to generation of water level correction information in the second embodiment. [Figure 17] 10 is an example of a flowchart showing the procedure of a process for generating water level correction information in the second embodiment. [Figure 18] FIG. 10 is a block diagram of an information processing device according to a third embodiment. [Figure 19] This is a diagram showing the position of a ship in three-dimensional Cartesian coordinates. [Figure 20] 1 shows an example of a schematic data structure of voxel data. [Figure 21] FIG. 2 is an example of a functional block diagram of a self-position estimation unit. [Figure 22] This is a view of the ship as seen from the rear. [Figure 23] (A) Shows the time change between the predicted water level and the measured water level at the location where the ship is located. (B) An example of the frequency distribution of multiple water level difference values ​​calculated within the immediately preceding specified period. [Figure 24] 13 is an example of a functional block of a predicted water level correction unit related to generation of water level correction information in the third embodiment. [Figure 25] 13 is an example of a flowchart showing the procedure of a process for generating water level correction information in the third embodiment. [Figure 26] 10 is a schematic configuration of an operation support system according to a fourth embodiment. [Figure 27] FIG. 10 is a block diagram illustrating an example of a hardware configuration of a server device according to a fourth embodiment. [Figure 28] (A) An example of the data structure of a predicted water level database. (B) An example of the data structure of measured water level information. [Figure 29] 10 is an example of a functional block diagram of a controller of a server device according to a fourth embodiment. [Figure 30] 10A is an example of a flowchart executed by an information processing device according to a third embodiment; and FIG. 10B is an example of a flowchart executed by a server device according to a third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] According to a preferred embodiment of the present invention, an information processing device includes a predicted water level information acquisition means for acquiring predicted water level information relating to predicted water levels at water level prediction points on one or more candidate routes that are candidates for a ship's navigation route, a bridge predicted water level calculation means for calculating, based on the predicted water level information, a bridge predicted water level that is the predicted water level at each bridge passing point on each of the candidate routes at the scheduled time of passage of the ship, and a determination means for determining, based on the bridge predicted water level, whether or not each of the candidate routes is suitable as a navigation route. According to this aspect, the information processing device can accurately grasp the water level at the scheduled time of passage of the ship for each bridge passing point on the candidate route, and can preferably determine whether or not the candidate route is suitable as a navigation route.

[0011] In one aspect of the information processing device, the determination means determines whether the ship can pass under the bridge based on the predicted bridge water level, and determines the suitability based on the result of the determination of the passability. According to this aspect, the information processing device can accurately determine whether the candidate route is suitable as a navigation route based on the determination result of the ship can pass under each bridge on the candidate route.

[0012] In another aspect of the information processing device, the determination means determines whether or not the vessel can pass under the bridge based on a bridge height that indicates the height of the bridge based on map data and a vessel highest point height that indicates the height of the vessel's highest point that is calculated based on the predicted bridge water level. According to this aspect, the information processing device can determine whether or not the vessel can pass under the bridge, taking into account the position of the vessel's highest point at the bridge passing point.

[0013] In another aspect of the information processing device, the information processing device further includes a vessel highest point height calculation means for calculating the vessel highest point height based on water surface measurement data, which is water surface measurement data measured by a measuring device installed on the vessel, highest point information relating to the height from the reference position of the vessel to the highest point of the vessel, and the bridge predicted water level. According to this aspect, the information processing device can accurately calculate the vessel highest point height, which is the height of the highest point of the vessel, using the bridge predicted water level.

[0014] In another aspect of the information processing device, the information processing device further includes a navigation route determination means for determining the navigation route based on the suitability determination result. According to this aspect, the information processing device can determine the navigation route that the ship should take. In a preferred example, when there are multiple candidate routes that have been determined to be suitable, the navigation route determination means may display a screen for selecting the navigation route from the candidate routes.

[0015] In another aspect of the information processing device, the information processing device further includes a candidate route acquisition means for acquiring a plurality of the candidate routes with different scheduled departure times, and the bridge predicted water level calculation means calculates the estimated passage time and the bridge predicted water level based on the predicted water level information and the scheduled departure time of each of the candidate routes. According to this aspect, the information processing device can determine the suitability of each of the candidate routes with different scheduled departure times as a navigation route, taking into account that water levels change with time.

[0016] In another aspect of the information processing device, the bridge predicted water level calculation means calculates the bridge predicted water level corresponding to the bridge passing point based on the predicted water levels at the water level prediction points that are respectively closest to the bridge passing point in the upstream and downstream directions. According to this aspect, the information processing device can accurately calculate the bridge predicted water level at the bridge passing point from the predicted water levels at the water level prediction points.

[0017] According to another preferred embodiment of the present invention, there is provided a determination method executed by a computer, which obtains predicted water level information relating to predicted water levels at water level prediction points where water levels are predicted along one or more candidate routes that are candidates for a ship's navigation route, calculates predicted bridge water levels, which are predicted water levels at each bridge crossing point at which the ship will pass a bridge on each of the candidate routes based on the predicted water level information, and determines whether each of the candidate routes is suitable as a navigation route based on the predicted bridge water levels. By executing this determination method, the computer can accurately determine whether the candidate routes are suitable as navigation routes.

[0018] According to yet another preferred embodiment of the present invention, a computer-executable program causes the computer to execute the following processes: acquire predicted water level information regarding predicted water levels at water level prediction points along one or more candidate routes for a ship's navigation route; calculate, based on the predicted water level information, predicted bridge water levels, which are predicted water levels at each bridge crossing point along each of the candidate routes at the scheduled time of passage of the ship; and determine, based on the predicted bridge water levels, whether each of the candidate routes is suitable as a navigation route. By executing this program, the computer can accurately determine whether the candidate routes are suitable as navigation routes. Preferably, the program is stored in a storage medium. [Example]

[0019] Preferred embodiments of the present invention will now be described with reference to the drawings.

[0020] <First Example> (1-1) Overview of the flight support system Figures 1 and 2 show the schematic configuration of a navigation assistance system according to a first embodiment. Specifically, Figure 1 shows a block diagram of the navigation assistance system, Figure 2(A) is a top view illustrating a ship included in the navigation assistance system and a field of view (range measurement range) 90 of a lidar 3 (described later), and Figure 2(B) is a rear view of the ship and the field of view 90 of the lidar 3. The navigation assistance system includes an information processing device 1 that moves with the ship, which is a mobile object, a sensor group 2 mounted on the ship, and a server device 7.

[0021] The information processing device 1 is electrically connected to the sensor group 2, and provides navigation support for the ship on which the information processing device 1 is installed, based on the outputs of various sensors included in the sensor group 2 and predicted water level information "D1" (described later) transmitted from the server device 7. In this embodiment, the information processing device 1 determines the navigation route of the ship by accurately determining in advance whether the ship can pass under a bridge. Note that navigation support may also include docking support such as automatic berthing (docking). The information processing device 1 may be a navigation device installed on the ship, or an electronic control device built into the ship.

[0022] The sensor group 2 includes various external and internal sensors provided on the ship. In this embodiment, the 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 the sensor group 2 may include a receiver that generates positioning results of a GNSS other than GPS instead of the GPS receiver 5. The information processing device 1 acquires the position of the ship on the water surface, which is required when referring to a river map database described later, from the GPS receiver 5 or the like.

[0023] The LIDAR 3 is an external sensor that emits a pulsed laser beam within a predetermined angular range in the horizontal direction (see FIG. 2(A)) and a predetermined angular range in the vertical direction (i.e., the direction of elevation and depression angles) (see FIG. 2(B)), thereby discretely measuring the distance to an object in the external world and generating three-dimensional point cloud data indicating the position of the object. In the examples of FIGS. 2(A) and 2(B), the ship is provided with two LIDARs 3, one facing the left side of the ship and the other facing the right side of the ship. Note that the number of LIDARs 3 installed on the ship is not limited to two, and may be one, three, or more. The LIDAR 3 includes 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 light receiving 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 identified based on the above-mentioned light receiving signal. Note that the LIDAR 3 is not limited to the above-mentioned scan-type LIDAR, but may also be a flash-type LIDAR that generates three-dimensional data by irradiating a diffused laser light into the field of view of a two-dimensional array sensor. The LIDAR 3 is an example of a "measurement device" in the present invention.

[0024] Furthermore, in this embodiment, the vertical range measured by the LIDAR 3 is a range that includes at least the area above the horizontal direction (i.e., the direction where the elevation angle is positive) and the area below the horizontal direction (i.e., the direction where 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 floats. Note that 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 direction and the measurement range of at least one LIDAR 3 includes the area below the horizontal direction.

[0025] The server device 7 transmits predicted water level information D1, which indicates water levels predicted as water levels at a predetermined time ahead at multiple points on a river (also referred to as "predicted water levels"), to the information processing device 1. Hereinafter, each point at which a predicted water level is calculated will also be referred to as a "water level prediction point." For example, when the server device 7 receives request information including the current position of the information processing device 1 from the information processing device 1, it transmits predicted water level information D1, which indicates the predicted water level at a water level prediction point located within a predetermined distance from the current position, to the information processing device 1. In another example, when the server device 7 receives information 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 of the water level prediction point on the specified river to the information processing device 1. Note that the water level (water surface height) differs depending on the location on the river, and the phase of the time change of the water level also differs depending on the location on the river.

[0026] (1-2) Device configuration 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 includes an interface 11, a memory 12, and a controller 13. These elements are connected to each other via a bus line.

[0027] The interface 11 performs interface operations related to the exchange of data between the information processing device 1 and an external device. In this embodiment, the interface 11 acquires output data from each sensor in the sensor group 2, such as the LIDAR 3 and the GPS receiver 5, and supplies the acquired data to the controller 13. The 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. The interface 11 also supplies, for example, signals related to vessel control generated by the controller 13 to each component of the vessel that controls the operation of the vessel. For example, the vessel includes a driving source such as an engine or an electric motor, a screw that generates a forward thrust based on the driving force of the driving source, a thruster that generates a lateral thrust based on the driving force of the driving source, and a rudder, which is a mechanism for freely determining the direction of travel of the vessel. During automatic operation, such as automatic docking, the interface 11 supplies control signals generated by the controller 13 to each of these components. If the vessel is equipped with an electronic control device, the interface 11 supplies the control signal generated by the controller 13 to the electronic control device. The interface 11 may be a wireless interface such as a network adapter for wireless communication, or may be a hardware interface for connecting to an external device via a cable, etc. The interface 11 may also perform interface operations with various peripheral devices such as an input device, a display device, and a sound output device.

[0028] The memory 12 is configured by various types of volatile and non-volatile memory, such as a RAM (Random Access Memory), a ROM (Read Only Memory), a hard disk drive, and a flash memory. The memory 12 stores programs for the controller 13 to execute predetermined processes. The programs executed by the controller 13 may be stored in a storage medium other than the memory 12.

[0029] The memory 12 also stores a river map database (DB) 10 and highest point information IH.

[0030] The river map DB10 stores feature data, which is data related to features (landmarks) that exist on or near rivers. The above-mentioned features include at least bridges located on rivers that are passable by ships. The feature data includes location information indicating the location of the feature, and attribute information indicating various attributes of the feature, such as the type and size. Note that the attribute information of the feature data corresponding to a bridge includes at least information related to the height (e.g., elevation) of the bridge's clearance (in other words, the bottom of the bridge above the river).

[0031] In addition to feature data, the river map DB10 may further include, for example, information on docking locations (including shores and piers) and information on waterways that ships can navigate. The river map DB10 may also be stored in an external storage device of the information processing device 1, such as a hard disk connected to the information processing device 1 via the interface 11. The storage device may be a server device that communicates with the information processing device 1. The storage device may also be composed of multiple devices. The river map DB10 may also be updated periodically. In this case, for example, the controller 13 receives partial map information on the area to which its own position belongs from a server device that manages map information via the interface 11, and reflects the information in the river map DB10.

[0032] The highest point information IH is information about the height of the part of the ship (highest point) that is located at the highest position in a 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 vertical 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 the LIDAR 3, and corresponds to, for example, the installation position of the LIDAR 3. The highest point information IH is generated based on prior measurement results and is pre-stored in memory 12.

[0033] The memory 12 also stores information necessary for the processing executed by the information processing device 1 in this embodiment, in addition to the river map DB 10. For example, the 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 scanning cycle. In another example, the memory 12 stores information regarding candidate routes for the navigation route that the ship should take.

[0034] 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 executes programs stored in the memory 12 or the like to perform processing related to operation support and the like.

[0035] In addition, 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.

[0036] The candidate route acquisition unit 15 acquires one or more routes (also referred to as "candidate routes") that are candidates for the ship's navigation route. In this case, the candidate route acquisition unit 15 may generate the candidate routes based on a route search process, or may acquire information representing the determined candidate routes from the memory 12 or from another device via the interface 11. When generating a candidate route, for example, the candidate route acquisition unit 15 searches for a route on a river from the departure point to the destination as a candidate route based on a pair of a departure point (which may be the current location) and a destination specified by input data supplied from an input device via the interface 11. In this case, the candidate route acquisition unit 15 searches for a candidate route 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 the ship can pass under bridges that exist on the route. In other words, it is not guaranteed that the generated candidate routes will allow the ship to pass under bridges that exist on the route.

[0037] The candidate route suitability determination unit 16 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 referred to as "bridge passability determination"), 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. Details of the bridge passability determination will be described later.

[0038] The navigation route determination unit 17 determines the navigation route based on the bridge passability determination result made by the candidate route suitability determination unit 16. In this case, the navigation route determination unit 17 recognizes candidate routes for which it has been determined that the ship can pass over all bridges on the route as candidate routes that are suitable as navigation routes (also referred to as "suitable candidate routes"), and determines the navigation route on which the ship should navigate from the recognized suitable candidate routes. Here, if there are multiple suitable candidate routes, the navigation route determination unit 17 may select the suitable candidate route with the shortest required time as the navigation route, or may cause the display unit to selectably display the suitable candidate routes via the interface 11 (i.e., display a selection screen for suitable candidate routes on the display unit), and determine the suitable candidate route selected by the input unit from the displayed suitable candidate routes as the navigation route.

[0039] In the first embodiment, the controller 13 functions as a "predicted water level information acquisition means," a "candidate route acquisition means," a "bridge predicted water level calculation means," a "ship highest point height calculation means," a "determination means," a "operation route determination means," and a computer that executes a program, etc.

[0040] 3(B) is a block diagram showing an example of the hardware configuration of a server device. The server device mainly includes an interface 71, a memory 72, and a controller 73. These elements are connected to each other via a bus line.

[0041] The interface 71 performs interface operations related to the exchange of data between the server device and external devices. In this embodiment, the interface 71 performs processing to transmit predicted water level information D1 to the information processing device 1 based on the control of the controller 73. In this case, the interface 71 may be a wireless interface such as a network adapter for wireless communication, or may be a hardware interface for connecting to an external device via a cable or the like. The interface 71 may also perform interface operations with various peripheral devices such as an input device, a display device, and a sound output device.

[0042] The memory 72 is configured by various types of volatile and non-volatile memory, such as RAM, ROM, a hard disk drive, and flash memory. The memory 72 stores programs for the controller 73 to execute predetermined processes. The programs executed by the controller 73 may be stored in a storage medium other than the memory 72.

[0043] The memory 72 also stores a predicted water level DB 70. The predicted water level DB 70 is a database that records predicted water levels at each water level prediction point on the river. In this case, for example, the predicted water level DB 70 associates, for each water level prediction point, location information of the water level prediction point with a predicted water level for each time (date and time) determined at a predetermined time interval. The predicted water level is determined by comprehensively taking into account past measurement results of a water level meter installed at the water level prediction point, the weather and atmospheric pressure up to the time when the water level is predicted, and other factors. The predicted water level DB 70 may be stored in a storage device external to the server device 7, such as a hard disk connected to the server device 7 via the interface 71. The storage device may be another server device that communicates with the server device 7. The storage device may also be composed of multiple devices. The predicted water level DB 70 may also be updated periodically.

[0044] The controller 73 includes one or more processors such as a CPU, a GPU, a TPU, etc., and controls the entire server device 7. In this case, the controller 73 executes a program stored in the memory 72, etc., to perform processing related to the distribution of the predicted water level information D1, etc.

[0045] (1-3) Candidate route suitability determination process Next, the process of determining a suitable candidate route (also referred to as "candidate route suitability determination process") will be described. The candidate route suitability determination unit 16 calculates the predicted water level (also referred to as "bridge predicted water level") under each bridge on the candidate route according to the scheduled time of passage of the ship. Then, based on the calculated bridge predicted water level, the candidate route suitability determination unit 16 determines whether the ship can pass under each bridge, and based on the determination result, determines whether the candidate route is a suitable candidate route.

[0046] FIG. 4(A) is a map showing the ship's departure point, destination, and bridges on the river. FIG. 4(B) is a map further showing a candidate route 91 determined to be a suitable candidate route. In the examples of FIGS. 4(A) and 4(B), there are bridges on the route from the departure point to the destination, and multiple candidate routes exist due to river branching and confluence. However, it is not always possible for a target ship to pass under all bridges on the map. Therefore, the candidate route suitability determination unit 16 calculates the predicted bridge water level for each bridge on the candidate route according to the scheduled time of passage of the ship, and determines whether the ship can pass under each bridge based on the calculated predicted bridge water level. In the examples of FIGS. 4(A) and 4(B), the candidate route suitability determination unit 16 determines that the ship can pass under each of the four bridges on the candidate route 91 based on the predicted bridge water level, and determines that the candidate route 91 is a suitable candidate route.

[0047] If there are multiple candidate routes, the candidate route suitability determination unit 16 determines whether or not a ship can pass under each bridge on each candidate route. Below, we will explain how to calculate the predicted bridge water level, and then we will explain how to determine whether or not a ship can pass under a bridge using the predicted bridge water level.

[0048] (1-3-1) Calculation of predicted bridge water levels FIG. 5 is an overhead view showing the vicinity of bridges B1 and B2 on a candidate route. Here, point "Pa1" is the water level prediction point closest to bridges B1 and B2 downstream, and point "Pa2" is the water level prediction point closest to bridges B1 and B2 upstream. Point "Pb1" is a point located directly below bridge B1 on the candidate route, and point "Pb2" is a point located directly below bridge B2 on the candidate route. Here, points Pb1 and Pb2 correspond to the passing points under the bridges (also called "bridge passing points") when a ship passes through the illustrated river.

[0049] In this case, the candidate route suitability determination unit 16 identifies the predicted water levels in time series at the water level prediction points Pa1 and Pa2, which are respectively closest to bridges B1 and B2 in the upstream and downstream directions, based on the predicted water level information D1. Figure 6(A) shows a graph "Ga1" that represents the transition of the predicted water level at water level prediction point Pa1, and a graph "Ga2" that represents the transition of the predicted water level at water level prediction point Pa2. As shown in Figure 6(A), the water level differs depending on the location on the river, and the phase of the time change of the water level differs depending on the location on the river.

[0050] Next, the candidate route suitability determination unit 16 calculates the bridge predicted water levels in a time series at the bridge passing point Pb1 corresponding to bridge B1 and the bridge passing point Pb2 corresponding to bridge B2 based on the predicted water levels in a time series at the water level prediction point Pa1 and the water level prediction point Pa2.

[0051] In this case, for example, the candidate route suitability determination unit 16 determines, for each prediction target time, a predicted bridge water level at the bridge passing point Pb1 by linear interpolation based on the distance between the water level prediction point Pa1 and the bridge passing point Pb1, the distance between the water level prediction point Pa2 and the bridge passing point Pb1, and the predicted water levels at the water level prediction points Pa1 and Pa2. Similarly, the candidate route suitability determination unit 16 determines, for each prediction target time, a predicted bridge water level at the bridge passing point Pb2 by interpolation (for example, linear interpolation) based on the distance between the water level prediction point Pa1 and the bridge passing point Pb2, the distance between the water level prediction point Pa2 and the bridge passing point Pb2, and the predicted water levels at the water level prediction points Pa1 and Pa2. Figure 6(B) shows graph "Gb1" representing the transition of the bridge predicted water level at bridge passing point Pb1, and graph "Gb2" representing the transition of the bridge predicted water level at bridge passing point Pb2, together with graphs Ga1 and Ga2. As shown in Figure 6(B), graph Gb1 representing the bridge predicted 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, and graph Gb2 representing the bridge predicted 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.

[0052] The candidate route suitability determination unit 16 may calculate the predicted bridge water level at each bridge passing point using any method other than linear interpolation (for example, spline interpolation or polynomial approximation). In this case, the candidate route suitability determination unit 16 may calculate the predicted water level at the target bridge passing point based on the predicted water levels at three or more water level prediction points near the target bridge passing point.

[0053] Next, the candidate route suitability determination unit 16 determines the estimated passage time, which is the time at which the vessel is scheduled to pass each bridge crossing point if the vessel is navigated along the candidate route, and recognizes the predicted bridge water level at the estimated passage time for each bridge crossing point. In this case, the candidate route suitability determination unit 16 predicts the time at which the vessel will pass each bridge crossing point if the vessel is navigated along the target candidate route based on the estimated departure time. In this case, for example, the candidate route suitability determination unit 16 calculates the estimated passage time at each bridge crossing point based on the estimated departure time, the estimated vessel speed, and the required navigation distance from the departure point to each bridge crossing point. Note that, for example, if the vessel is scheduled to depart immediately, the candidate route suitability determination unit 16 recognizes the current time as the estimated departure time; otherwise, it recognizes the time specified by input data supplied from the input device via the interface 11 as the estimated departure time. Then, the candidate route suitability determination unit 16 reads the predicted bridge water level at the scheduled time of passage for each bridge passing point from the predicted bridge water level in time series calculated by interpolation.

[0054] As described above, the candidate route suitability determination unit 16 can accurately calculate the predicted bridge water level for each bridge existing on the candidate route based on the predicted water level information D1.

[0055] (1-3-2) Determining whether or not a bridge can be passed based on the predicted water level The candidate route suitability determination unit 16 predicts the vertical distance (also called "predicted distance") between the ship and the bridge at the bridge passing point based on the predicted bridge water level at the scheduled time of passage, and determines whether or not the ship can pass over each bridge based on this predicted distance. A specific example of this process will be described with reference to Figure 7.

[0056] FIG. 7 is a diagram outlining a method for calculating the prediction interval, showing a ship observed from behind when it is assumed to be at a bridge crossing point. In the example of FIG. 7, two lidars 3 are installed on the ship, and a position at the same height as the lidars 3 is defined as the ship's reference position. The ship also has a protrusion 33, which is the highest point of the ship. The bridge 30 has a lower girder 32 that forms the bottom surface of the structure located above the river, forming an under-girder space 31 through which the ship can pass. Line L1 indicates the origin of the height (e.g., elevation) used for predicting bridge water levels, etc. Line L2 indicates the water surface position, and line L3 indicates a position at the same height as the ship's reference position. Line L4 indicates a position at the same height as the ship's highest point, and line L5 indicates a position at the same height as the lower girder 32 that forms the bottom surface of the bridge 30 above the river. Furthermore, measurement points "m5" to "m8" indicate measurement points on the water surface measured by the lidar 3.

[0057] First, the candidate route suitability determination unit 16 extracts feature data corresponding to the bridge 30 over which the ship is scheduled to pass from the river map DB 10, and identifies the height of the bridge 30 (the height corresponding to arrow A2, also called "bridge height") by referring to the extracted feature data. Note that the bridge height represents the height of the bridge's bottom surface above the river (i.e., the height of the part below the girders). The predicted bridge water level of the bridge 30 is the height corresponding to arrow A1.

[0058] The candidate route suitability determination unit 16 also calculates the height distance from the ship reference position to the water surface (the distance corresponding to arrow A3, also referred to as the "water surface distance") based on point cloud data (also referred to as "water surface measurement data") from the LIDAR 3, which measured the water surface at any time before the navigation route was determined. In this case, the candidate route suitability determination unit 16 extracts 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, to exclude points that detect locations other than the water surface, such as bridge piers, quays, and other ships, the minimum z-coordinate value of the point cloud data is calculated, and the water surface measurement data can be obtained by extracting the z-coordinate value of each measurement point closest to that minimum value. In Figure 7, the candidate route suitability determination unit 16 considers data corresponding to measurement points "m5" to "m8" on the water surface to be water surface measurement data and extracts them from the point cloud data from the LIDAR 3. Then, the candidate route suitability determination unit 16 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. Note that the candidate route suitability determination unit 16 may calculate the water surface distance multiple times before determining the navigation route, and use the average value of the multiple calculation results of the water surface distance as the water surface distance in subsequent processing. Note that while Figure 7 shows only measurement points "m5" to "m8" of the LIDAR 3 on one side (the right side), in reality, point cloud data of the LIDAR 3 on both sides (the right side and the left side) is used.

[0059] Next, the candidate route suitability determination unit 16 identifies the height width (see arrow A4) from the ship reference position to the highest point by referring to the highest point information IH from the memory 12. Then, the candidate route suitability determination unit 16 calculates the height of the ship's highest point (the height corresponding to arrow A5, also called the "ship's highest point height"), which corresponds to the height obtained by adding the predicted bridge water level (see arrow A1) to the water surface distance (see arrow A3) and the height width (see arrow A4) from the ship's reference position to the highest point.

[0060] Then, the candidate route suitability determination unit 16 calculates the width obtained by subtracting the vessel's highest point height from the bridge height as the prediction interval (see arrow A6).

[0061] Thereafter, the candidate route suitability determination unit 16 determines that the vessel can pass the bridge if the calculated prediction interval is equal to or greater than a threshold value (also referred to as the "prediction interval threshold Th"), and determines that there is a risk that the vessel will not be able to pass the bridge safely if the prediction interval is less than the prediction interval threshold Th. The prediction interval threshold Th may be set to a fixed value stored in advance in the memory 12 or the like, or may be set to a variable value. In the latter case, the candidate route suitability determination unit 16 may determine the prediction interval threshold Th based on, for example, an index (e.g., standard deviation) that represents the variation in the height direction values ​​of each piece of water surface measurement data used to calculate the water surface distance. In this case, the candidate route suitability determination unit 16 takes into consideration that, for example, when wave height is large, the vessel's vertical movement in the height direction also becomes large, and the variation in the water surface measurement data also becomes large, and sets the prediction interval threshold Th to be larger as the above-mentioned standard deviation increases.

[0062] In this way, the candidate route suitability determination unit 16 can accurately determine whether or not each bridge on the candidate route can be passed through, based on the bridge predicted water level, the point cloud data output by the lidar 3, and the map data related to the bridge.

[0063] The candidate route suitability determination unit 16 then preferably stores information associating the estimated passage time, bridge predicted water level, and prediction interval for each bridge on the candidate route in the memory 12 or the like. FIG. 8 shows a table associating the estimated passage time, bridge predicted water level, and prediction interval for each bridge (Bridge B1, Bridge B2, Bridge B3, ...) on the candidate route. As shown in FIG. 8, the candidate route suitability determination unit 16 calculates the estimated passage time, bridge predicted water level, and prediction interval for all bridges (Bridge B1, Bridge B2, Bridge B3, ...) on the candidate route and stores the calculation results in the memory 12 or the like. In the above table, each bridge may be represented by location information or other identifiable information indicating the location of the bridge instead of or in addition to the name of Bridge B1, Bridge B2, Bridge B3, etc.

[0064] (1-4) Functional Blocks Fig. 9 shows an example of functional blocks of the controller 13 in the first embodiment. Functionally, the candidate route suitability determination unit 16 has a bridge height acquisition unit 61, a bridge predicted water level calculation unit 62, a water surface distance calculation unit 63, a vessel highest point height calculation unit 64, a prediction interval calculation unit 65, and a determination unit 66. Note that in Fig. 9, blocks where data is exchanged are connected by solid lines, but the combination of blocks where data is exchanged is not limited to this. The same applies to other functional block diagrams described later.

[0065] 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. The information about the candidate routes that the candidate route acquisition unit 15 supplies to the bridge predicted water level calculation unit 62 includes information necessary for calculating the estimated time of passing each bridge (for example, information about the departure time).

[0066] The bridge height acquisition unit 61 extracts feature data corresponding to bridges existing on the candidate route from the river map DB 10, and acquires the bridge height of each bridge existing on the candidate route based on the extracted feature data.

[0067] Furthermore, the bridge predicted water level calculation unit 62 calculates the estimated time of passage and the bridge predicted water level at the bridge passing 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 information on the candidate route supplied from the candidate route acquisition unit 15. Furthermore, the water surface distance calculation unit 63 calculates the water surface distance based on water surface measurement data measuring the water surface (i.e., below the horizontal direction).

[0068] 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 bridge water level at the bridge passing point corresponding to each bridge on the candidate route, and the vertical width from the ship's reference position to the highest point indicated by the highest point information IH.

[0069] 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 vessel's highest point height calculated by the vessel's highest point height calculation unit 64. The determination unit 66 determines whether each bridge on the candidate route is passable or not 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 a candidate route that allows the vessel to pass all bridges on the candidate route as a suitable candidate route, and supplies information on the determined suitable candidate route to the navigation route determination unit 17.

[0070] The navigation route determination unit 17 determines a navigation route based on the information on the suitable route candidates provided by the determination unit 66. In this case, if there is only one suitable route candidate, the navigation route determination unit 17 selects the suitable route candidate as the navigation route. On the other hand, if there are multiple suitable routes candidate, the navigation route determination unit 17 may select the suitable route candidate with the shortest travel time as the navigation route, or may select one navigation route based on an index other than travel time (e.g., the width of the river or the number of vessels in operation). In another example, the navigation route determination unit 17 may display suitable route candidates selectable on a display unit electrically connected via the interface 11, and select a suitable route candidate specified by input data entered via the interface 11 from an input device operated by a user. In this case, the navigation route determination unit 17 refers to the river map DB 10 to generate display information for a screen that displays the suitable route candidate superimposed on a map of the area including the departure point and destination, and provides the generated display information to the display device via the interface 11.

[0071] (1-5) Processing flow 10 is an example of a flowchart of a process for determining a navigation route. The controller 13 executes the process of the flowchart, for example, after starting the ship and detecting a user input instructing determination of a navigation route.

[0072] First, the candidate route acquisition unit 15 acquires a candidate route (step S11). In this case, for example, the candidate route acquisition unit 15 searches for a candidate route by performing a route search process based on a destination and a departure point (or a current location) input by an input device operated by a user via the interface 11. In another example, the candidate route acquisition unit 15 may acquire a candidate route by accepting a designation of the candidate route from the input device via the interface 11, or, if the candidate route is stored in the memory 12, by reading the candidate route from the memory 12.

[0073] Next, the candidate route suitability determination unit 16 receives predicted water level information D1 indicating predicted water levels at water level prediction points on each candidate route from the server device 7 (step S12). Then, based on the planned departure time and the predicted water level information D1 acquired in step S12, the candidate route suitability determination unit 16 calculates the planned passage time and the predicted bridge water level at each bridge passing point on each candidate route (step S13).

[0074] Then, the candidate route suitability determination unit 16 acquires the bridge heights 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 of the lidar 3 measuring downward (i.e., the direction where the depression angle is positive) (step S15). Note that the candidate route suitability determination unit 16 may use a representative value, such as the average value of multiple calculation results of the water surface distance obtained by performing step S15 multiple times, as the value of the water surface distance in subsequent processing. Also, steps S11 to S15 may be performed in any order.

[0075] The candidate route suitability determination unit 16 then calculates the vessel's highest point height at the bridge passing point for each bridge on each candidate route based on the bridge predicted water level, water surface distance, and highest point information IH (step S16). The candidate route suitability determination unit 16 then calculates a prediction interval for each bridge on each candidate route based on the bridge height and the vessel's highest point height, and determines whether the vessel can pass under each bridge on each candidate route based on the prediction interval (step S17). The operation route determination unit 17 then selects, as the operation route, a suitable candidate route that allows passage over all bridges on the route based on the determination result by the candidate route suitability determination unit 16 in step S17 (step S18).

[0076] (1-6) Variations The following describes preferred modifications of the above-described embodiment. The following modifications may be applied to these embodiments in combination.

[0077] The candidate route acquisition unit 15 may generate multiple candidate routes for the same route with different departure times.

[0078] For example, in step S11 of the flowchart in FIG. 10, when there are N routes (N is an integer equal to or greater than 1) from the departure 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 that differ 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 estimated passage time and predicted bridge water level at each bridge passing point for each candidate route. Note that even if the candidate routes differ only in departure time, the estimated passage time and predicted bridge water level at each bridge passing point will be different. Then, the steps other than the above-mentioned steps S11 and S13 are executed in the same manner as in the above-mentioned embodiment.

[0079] According to this modification, the information processing device 1 can increase the number of candidate routes and determine a more suitable operating route.

[0080] As described above, the controller 13 of the information processing device 1 according to the first embodiment acquires predicted water level information D1 relating to predicted water levels at water level prediction points along one or more candidate routes for a ship's navigation route. Based on the predicted water level information D1, the controller 13 then calculates predicted bridge water levels, which are predicted water levels at the scheduled time of ship passage at each bridge crossing point on each candidate route where the ship will pass. Based on the predicted bridge water levels, the controller 13 then determines whether each candidate route is suitable as a navigation route. This allows the information processing device 1 to accurately grasp the water level at the scheduled time of ship passage for each bridge crossing point on the candidate route, and to appropriately determine whether the candidate route is suitable as a navigation route.

[0081] <Second Example> The information processing device 1 according to the second embodiment measures the water level at the predicted water level points that the ship will pass after it starts sailing according to the determined navigation 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. This allows the information processing device 1 to accurately determine whether the ship can pass under bridges that exist on the navigation route but have not yet been passed (for example, to make a final check as to whether the ship can actually pass under them). Hereinafter, the same components as those in the first embodiment will be appropriately designated by the same reference numerals, and their explanation will be omitted.

[0082] (2-1) Block configuration 11 is a block diagram of an information processing device 1A according to Example 2. As shown in the figure, the information processing device 1A includes an interface 11, a memory 12, and a controller 13.

[0083] The memory 12 stores the river map DB 10 and highest point information IH described in the first embodiment. The river map DB 10 may also include feature data on any feature (landmark) other than a bridge whose height (e.g., elevation) is known. This feature data includes information on at least the location of the feature and its height (e.g., elevation). Hereinafter, features (including bridges) whose feature data is registered in the river map DB 10 will also be referred to as "registered features."

[0084] The controller 13 functionally includes a bridge passability determination unit 16A and a predicted water level correction unit 18A.

[0085] The bridge passage possibility determination unit 16A determines whether the ship can pass under bridges that exist on the determined navigation route. Furthermore, when water level correction information is supplied from the predicted water level correction unit 18A (described later), the bridge passage possibility determination unit 16A corrects the predicted water level at each water level prediction point on the navigation route based on the water level correction information, and determines whether the ship can pass under each bridge on the navigation route based on the corrected predicted water level.

[0086] The predicted water level correction unit 18A generates water level correction information for correcting 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 passability determination unit 16A.

[0087] The controller 13 according to the second embodiment functions as a "ship reference height calculation means," a "measured water level calculation means," a "correction information generation means," a "bridge water level calculation means," a "bridge passability determination means," and a computer that executes a program.

[0088] The information processing device 1A may or may not execute the process related to determining the navigation route described in the first embodiment. In the latter case, for example, the information processing device 1A stores navigation route information related to the navigation route in advance in the memory 12 or the like, and provides navigation support for the ship based on the navigation route recognized by referring to the navigation route information. In this case, the information processing device 1A may determine the navigation route based on input data specifying the navigation route supplied via the interface 11 from an input device operated by the user.

[0089] (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 described. In summary, the predicted water level correction unit 18A calculates the height of the ship reference position (also called "ship reference height") based on data measured by the LIDAR 3 from registered features on the navigation route. The predicted water level correction unit 18A then generates water level correction information based on the comparison result between the water level calculated based on the water surface distance and the ship 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 reference height corresponds to the water level calculated based on data measured by the LIDAR 3 from registered features, and will hereinafter also be called "measured water level".

[0090] Figure 12 is an overhead view showing the vicinity of bridges B3 and B4 located on the navigation route. Here, point "Pa3" is the water level prediction point closest to bridges B3 and B4 downstream, and point "Pa4" is the water level prediction point closest to bridges B3 and B4 upstream. Furthermore, points "Pb3" and "Pb4" are points (also called "water level measurement points") where the vessel reference height and measured water level are calculated. Furthermore, dashed line 70 indicates the navigation route.

[0091] First, the calculation of the ship reference height 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 bridge B3, which is a registered feature, is present within the measurement range of the LIDAR 3, based on the ship's position information obtained from the GPS receiver 5 etc. and the position information of registered features included in the river map DB 10. The predicted water level correction unit 18A then extracts data (also referred to as "feature measurement data") measured of the registered feature (here, bridge B3) from the point cloud data generated by the LIDAR 3, and calculates the ship reference height based on the extracted feature measurement data.

[0092] Figure 13 is a diagram of a ship observed from behind when a bridge B3, a registered feature, is within the measurement range of the LIDAR 3. Line L11 indicates the origin position of the height (e.g., elevation) used in bridge predicted water levels, etc., line L12 indicates the water surface position, and line L13 indicates a position at the same height as the ship's reference position. Line L14 indicates a position at the same height as the target registered feature (here, bridge B3) registered in the river map DB10. Furthermore, measured points "m9" to "m12" indicate the measured points of the registered feature (here, bridge B3) measured by the LIDAR 3.

[0093] In this case, for example, when the distance between the current position of the vessel and the position of a registered feature (here, bridge B3) (more specifically, the position of the registered feature registered in the river map DB 10) is within the maximum measurement distance of the LIDAR 3, the predicted water level correction unit 18A 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, to exclude points that have detected locations other than bridges, such as bridge piers, the maximum z-coordinate value of the point cloud data is calculated, and the bridge measurement data can be obtained by extracting the z-coordinate value of each measurement point that is closest to that maximum value. In Figure 13, the predicted water level correction unit 18A considers data corresponding to measurement points m9 to m12 of the girder lower part 32 to be 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 (the distance corresponding to arrow A12, also called the "feature distance") between the registered feature (here, bridge B3) and the ship reference position.The predicted water level correction unit 18A then identifies the height (the height corresponding to arrow A11, also called the "feature height") of the target registered feature registered in the river map DB10, and calculates the ship reference height, which is the height corresponding 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.

[0094] Next, the predicted water level correction unit 18A calculates the measured water level at the water level measurement point Pb3 based on the ship reference height based on the feature measurement data obtained at the water level measurement point Pb3 and the water surface measurement data obtained at the 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.

[0095] Then, the predicted water level correction unit 18A calculates the ship reference height and the measured water level at the water level measurement point Pb4 using the same processing procedure as for the water level measurement point Pb3.

[0096] Next, the predicted water level correction unit 18A calculates continuous measured water levels (also referred to as "interpolated measured water levels") along the navigation route that the vessel has passed by performing an arbitrary interpolation process using the measured water levels at water level measurement points that the vessel has passed through (i.e., water level measurement points where the measured water levels have been calculated), including water level measurement point Pb3 and water level measurement point Pb4. In this case, the predicted water level correction unit 18A may calculate continuous interpolated measured water levels along the navigation route that the vessel has passed through based on an arbitrary interpolation method, such as linear interpolation, spline interpolation, or polynomial approximation. In this case, if model information indicating a model that represents the water levels along the navigation route is pre-stored in the memory 12 or the like, the predicted water level correction unit 18A may determine the parameters of the model by fitting the measured water levels at the water level measurement points to the model. In this case, the predicted water level correction unit 18A recognizes the interpolated measured water levels at any point along the navigation 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 for each section of the river. The model information may also be part of the river map DB 10.

[0097] Then, each time a water level prediction point is passed, the predicted water level correction unit 18A compares the predicted water level at the passed water level prediction point with the interpolated measured water level, and calculates the difference value between the predicted water level and the interpolated measured water level (also called the "water level difference value"). Hereinafter, for convenience, the water level difference value is 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 multiple passed water level prediction points, and uses the calculated average value, etc., as a correction amount for the bridge predicted water level.

[0098] Figure 14(A) shows the change over time in predicted water levels and measured water levels at a location where a ship is located. Here, dashed circles 60a to 60k represent the measured water levels at each water level measurement point passed by the ship, and solid circles 68a to 68e represent the predicted water levels at each water level prediction point passed by the ship. Graph 69 is a graph showing interpolated measured water levels generated by interpolating the measured water levels indicated by dashed circles 60a to 60k. The arrows corresponding to the solid circles 68a to 68e each indicate a width corresponding to the water level difference value.

[0099] As shown in Figure 14(A), the predicted water level correction unit 18A calculates successive interpolated measured water levels on the navigation route that has already been passed by performing interpolation, and calculates water level difference values ​​that correspond to the differences between the interpolated measured water levels at the water level prediction points that have already been passed and the predicted water levels. In the example of Figure 14(A), the predicted water level correction unit 18A calculates water level difference values ​​at five points corresponding to solid circle 68a to 68e.

[0100] 14(B) is an example of a distribution showing the frequency (i.e., the frequency) of a plurality of water level difference values ​​calculated within a specified period immediately preceding the current time. The predicted water level correction unit 18A aggregates the water level difference values ​​calculated for each water level prediction point that has already been passed, and calculates statistics such as the average and standard deviation of the water level difference values. The predicted water level correction unit 18A then supplies the calculated average and standard deviation of the water level difference values ​​to the bridge passability determination unit 16A as water level correction information required for correcting the bridge predicted water level.

[0101] The bridge passage possibility determination unit 16A then corrects the predicted water level at each water level prediction point on the navigation route based on the water level correction information received from the predicted water level correction unit 18A, and calculates the estimated passage time and bridge predicted water level at each bridge passing point on the navigation route that the ship 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), using processing similar to that performed by the bridge predicted water level calculation unit 62 in the first embodiment. Hereinafter, the bridge predicted water level calculated based on the corrected predicted water level will also be referred to as the "bridge corrected water level." Then, the bridge passage possibility determination unit 16A calculates the ship's highest point height based on the water surface distance, bridge corrected water level, and highest point information IH using processing similar to that performed by the ship's highest point height calculation unit 64, and further calculates the prediction interval from the ship's highest point height using processing similar to that performed by the prediction interval calculation unit 65.

[0102] Preferably, the bridge passage possibility determination unit 16A stores information associating the calculated estimated passage time, bridge corrected water level, and prediction interval for each bridge on the navigation route that the ship has not yet passed in the memory 12, etc. Figure 15 shows a table associating the estimated passage time, bridge corrected water level, and prediction interval for each bridge (bridge B5, bridge B6, bridge B7, ...) on the navigation route that the ship has not yet passed. As shown in Figure 15, the candidate route suitability determination unit 16 calculates the estimated passage time, bridge corrected water level, and prediction interval for all bridges (bridge B5, bridge B6, bridge B7, ...) on the navigation route that the ship has not yet passed, and stores the calculation results in the memory 12, etc.

[0103] (2-3) Functional Blocks 16 shows an example of functional blocks of the predicted water level correction unit 18A related to the generation of water level correction information in Example 2. The predicted water level correction unit 18A functionally includes a feature height acquisition unit 81, a feature 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.

[0104] The feature height acquisition unit 81 acquires feature information corresponding to registered features that are located within a predetermined distance from the ship from the river map DB 10, and acquires the feature heights included in the feature information. In this case, the feature height acquisition unit 81 identifies the registered features based on, for example, current position information based on the GPS receiver 5 or the like and position information included in the feature information.

[0105] The feature distance calculation unit 82 extracts feature measurement data corresponding to the above-mentioned registered features from the point cloud data output by the LIDAR 3, and calculates feature distances based on the extracted feature measurement data. The points where feature measurement data is generated correspond to water level measurement points.

[0106] The ship reference height calculation unit 83 calculates the ship reference height based on the feature height acquired by the feature height acquisition unit 81 and the feature distance calculated by the feature distance calculation unit 82.

[0107] The water surface distance calculation unit 84 extracts water surface measurement data measuring 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 continuous interpolated measured water levels from the departure point of the navigation route to the present (more specifically, to the point of calculation of the immediately preceding measured water level) from the measured water levels calculated in the past at the water level measurement points.

[0108] The water level correction information generating unit 86 compares the predicted water level at the water level prediction point that the ship has passed with the interpolated measured water level based on the continuous interpolated measured water level calculated by the measured water level calculating unit 85 and the predicted water level information D1, and calculates the water level difference value.The water level correction information generating unit 86 then generates water level correction information including the average value of the water level difference value at the water level prediction point that the ship has passed and other statistics, and supplies the generated water level correction information to the bridge passage possibility determining unit 16A.

[0109] (2-4) Processing flow 17 is an example of a flowchart showing the procedure of the process of generating water level correction information executed by the predicted water level correcting unit 18A in Example 2. The predicted water level correcting unit 18A repeatedly executes the process of the flowchart.

[0110] First, the predicted water level correction unit 18A calculates the feature distance based on feature measurement data extracted from point cloud data generated by the LIDAR 3 when a registered feature is present 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 DB 10 (step S22).

[0111] Then, the predicted water level correction unit 18A calculates the measured water level (step S23) based on the ship 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. 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 or the like.

[0112] 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 the water level prediction point and the interpolated measured water level at the water level prediction point (step S24).

[0113] As described above, the controller 13 of the information processing device 1A according to the second embodiment calculates the ship 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 water surface height, based on the water surface measurement data, which is the water surface measurement data measured by the LIDAR 3, and the ship reference height. The controller 13 then generates water level 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 the river, and the measured water level calculated at the point closest to the water level prediction point corresponding to the predicted water level. Therefore, by using the generated water level correction value, it is possible to accurately estimate the water level at bridge points on a candidate route. As a result, the information processing device 1A generates water level correction information that accurately corrects the predicted water level even in a mode in which high-precision self-position estimation in the vertical direction is not performed, and can accurately re-determine whether the ship can pass under bridges that have not yet been passed on the water level predicted navigation route.

[0114] <Third Example> The information processing device 1 according to the third embodiment differs from the second embodiment in that it acquires the ship reference height by performing a highly accurate self-position estimation process including the height direction. Hereinafter, the same components as those in the second embodiment will be appropriately designated by the same reference numerals, and their description will be omitted.

[0115] (3-1) Block configuration 18 is a block diagram of an information processing device 1B according to Example 3. As shown in the figure, the information processing device 1B has an interface 11, a memory 12, and a controller 13.

[0116] The interface 11 acquires output data from each sensor in the sensor group 2 and supplies it to the controller 13. The sensor group 2 includes a lidar 3, a speed sensor 4 that detects the speed of the ship, a GPS receiver 5, and an inertial measurement unit (IMU) 6 that measures the acceleration and angular velocity of the target moving ship in three axial directions. The speed sensor 4 may be, for example, a speedometer that uses Doppler or a speedometer that uses GNSS.

[0117] The memory 12 stores the river map DB 10 and the highest point information IH described in the first embodiment. The river map DB 10 includes voxel data VD.

[0118] The voxel data VD is data that records position information and the like of stationary structures for each voxel, which represents a cube (regular lattice), the smallest unit in three-dimensional space. The voxel data VD includes data that expresses measured point cloud data of stationary structures in each voxel using a normal distribution, and is used for scan matching using NDT (Normal Distribution Transform), as described below. The information processing device 1B estimates, for example, the ship's planar position, height position, yaw angle, pitch angle, and roll angle through NDT scan matching. Unless otherwise specified, the self-position is assumed to also include the ship's attitude angle, such as yaw angle. The voxel data VD may be part of the river map DB 10.

[0119] The controller 13 functionally includes a bridge passability determination unit 16B, a predicted water level correction unit 18B, and a self-position estimation unit 19B.

[0120] The bridge passage possibility determination unit 16B determines whether it is possible to pass under bridges that exist on the determined operation route. The processing executed by the bridge passage possibility determination unit 16B is the same as that executed by the bridge passage possibility determination unit 16A in the second embodiment. The predicted water level correction unit 18B generates water level correction information for correcting 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 possibility determination unit 16B.

[0121] The self-position estimation unit 19B estimates its own position by performing scan matching based on NDT (NDT scan matching) based on point cloud data based on the output of the LIDAR 3 and voxel data VD corresponding to the voxels 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 may be point cloud data obtained by downsampling the point cloud data. The controller 13 according to the third embodiment functions as a "ship reference height calculation means," a "measured water level calculation means," a "correction information generation means," a "bridge water level calculation means," a "bridge passage possibility determination means," and a computer that executes a program.

[0122] (3-2) NDT Scan Matching Next, the position estimation based on NDT scan matching executed by the self-position estimation unit 19B will be described.

[0123] FIG. 19 is a diagram showing the position of a ship in three-dimensional Cartesian coordinates. As shown in the figure, the ship's own position on a plane defined on the three-dimensional Cartesian coordinates of xyz is expressed 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 axis of the ship's traveling direction, the pitch angle θ is defined as the elevation angle of the ship's traveling direction relative to the xy plane, and the yaw angle ψ is defined as the angle between the ship's traveling direction and the x-axis. The coordinates (x, y, z) are, for example, absolute positions 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.

[0124] Next, we will explain the voxel data VD used in NDT scan matching. The voxel data VD includes data in which measured point cloud data of a stationary structure in each voxel is expressed using a normal distribution.

[0125] 20 shows an example of a schematic data structure of the voxel data VD. The voxel data VD includes information on parameters when a point group in a voxel is expressed by a normal distribution, and in this embodiment includes, for each voxel, a "voxel ID," "voxel coordinates," "attribute information," "mean vector," and "covariance matrix."

[0126] "Voxel ID" indicates identification information for each voxel. "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 since its shape and size are predetermined, it is possible to identify the space of each voxel using its voxel coordinates. Voxel coordinates may also be used as a voxel ID.

[0127] "Attribute information" indicates information related to the attributes of the target voxel. For example, in this embodiment, the "attribute information" of a voxel corresponding to a bridge includes information indicating that it is a bridge part. Note that the "attribute information" of a voxel corresponding to the underside of the bridge girder (i.e., the bottom surface of the structural part above the river) may further include information indicating that it is the underside of the girder.

[0128] The "mean vector" and "covariance matrix" refer to the mean vector and covariance matrix, which are parameters when expressing the point cloud in the target voxel as a normal distribution. Note that the coordinates of an arbitrary point "i" in an arbitrary voxel "n" are X n (i)=[x n (i), y n (i), z n (i)] T and the number of points in voxel n is defined as "N n ", then the mean vector at voxel n is "μ n ” and the covariance matrix “V n " are expressed by the following formulas (1) and (2), respectively.

[0129]

number

[0130]

number

[0131] Next, an overview of NDT scan matching using voxel data VD will be explained.

[0132] Scan matching using NDT assuming a ship is performed using estimated parameters that are based on the amount of movement in three-dimensional space (here, xyz coordinates) and the orientation of the ship. P=[t x , t y , t z , t φ , t θ , t ψ ]T Here, "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 " indicates the yaw angle.

[0133] In addition, the coordinates of the point cloud data output by the lidar 3 are X L (j)=[x n (j), y n (j), z n (j)] T Then, X L The average value of (j) "L' n " is expressed by the following equation (3).

[0134]

number

[0135] Then, the self-position estimation unit 19B searches for voxel data VD associated with the point cloud data converted into the world coordinate system. Here, the world coordinate system is an absolute coordinate system adopted in the map DB 10 (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 position (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, and suppresses a decrease in position estimation accuracy caused by an error in association.

[0136] Then, the self-position estimation unit 19B calculates the mean vector μ n and the covariance matrix V nUsing the above, the evaluation function value (also called "individual evaluation function value") for matching of voxel n is calculated as "E n " is calculated.

[0137] In this case, the self-position estimation unit 19B calculates the individual evaluation function value E of the voxel n based on the following equation (4): n Calculate.

[0138]

number

[0139] Then, the self-position estimation unit 19B calculates a comprehensive evaluation function value (also called a "score value") "E(k)" for all voxels to be matched, as shown in the following formula (5). The score value E is an index showing the compatibility of the match.

[0140]

number

[0141]

number

[0142] 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 has a dead reckoning unit 91, a coordinate conversion unit 92, a water surface reflection data removal unit 93, and an NDT position calculation unit 94.

[0143] 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 moving speed and angular velocity of the ship based on the outputs of the speed sensor 4, the IMU 6, etc. to determine the moving distance and change in heading from the previous time. Then, the dead reckoning unit 91 calculates the estimated self-position X at time k-1, which is the processing time immediately preceding the current processing time k. ^ (k-1), the DR position X at time k, which is the distance traveled since the previous time and the change in direction DR (k) is calculated. DR (k) is the self-position obtained at time k based on dead reckoning, and the predicted self-position X - (k) corresponds to the estimated self-position X at time k-1 immediately after the start of self-position estimation. ^ If there is no DR position X (k-1), the dead reckoning unit 91 determines the DR position X based on the signal output by the GPS receiver 5, for example. DR (k) is defined.

[0144] The coordinate conversion unit 92 converts the point cloud data based on the output of the LIDAR 3 into a world coordinate system, which is the same coordinate system as the map DB 10. In this case, the coordinate conversion unit 92 performs coordinate conversion 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. Note that the process of converting point cloud data in a coordinate system based on a LIDAR installed on a moving body (a ship in this embodiment) into the coordinate system of the moving body, and the process of converting from the coordinate system of the moving body to the world coordinate system are disclosed in, for example, International Publication WO2019 / 188745.

[0145] The water surface reflection data removal unit 93 removes data (also referred to as "water surface reflection data") erroneously generated by the LIDAR 3 receiving light reflected from the water surface from the point cloud data supplied from the coordinate conversion 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 below) (i.e., positions with the same or lower z coordinate value) from the point cloud data as water surface reflection data. Note that the water surface reflection data removal unit 93 may estimate the water surface position based on the z coordinate value after coordinate conversion 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 obtained by removing the data below the water surface position from the point cloud data supplied from the coordinate conversion unit 92 to the NDT position calculation unit 94.

[0146] 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 expressed in the same world coordinate system, thereby associating the point cloud data with the voxels. Then, the NDT position calculation unit 94 calculates an individual evaluation function value based on equation (4) for each voxel associated with the point cloud data, and calculates an estimated parameter P that maximizes the score value E(k) based on equation (5). Then, the NDT position calculation unit 94 calculates the DR position X output by the dead reckoning unit 91 based on equation (6). DR (k), the NDT position X at time k is determined by applying the estimated parameter P obtained at time k. NDT The NDT position calculation unit 94 calculates the NDT position X NDT (k) is output as the estimated self-position X^(k) at time k.

[0147] (3-3) Generation of water level correction information The predicted water level correction unit 18B calculates the measured water level based on the ship reference height obtained by self-position estimation and the water surface distance based on the 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.

[0148] FIG. 22 is a view of the ship observed from behind. Line L21 indicates the origin position of the height (e.g., elevation) used in bridge predicted water levels, etc., line L22 indicates the water surface position, and line L23 indicates the position at the same height as the ship reference position. Furthermore, measurement points "m13" to "m16" indicate measurement 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 of the first embodiment based on the water surface measurement data corresponding to the water surface measurement points "m13" to "m16." Then, the predicted water level correction unit 18B calculates the height (i.e., the height indicated by arrow A23) obtained by subtracting the water surface distance of arrow A22 from the ship reference height, which is the height corresponding to arrow 21 obtained by self-location estimation, as the measured water level.

[0149] Figure 23(A) shows the change over time in the predicted water level and the measured water level at a point where a ship 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 the water surface measurement data. As mentioned above, it is possible to calculate the measured water level from the self-position estimation and the water surface distance each time the LIDAR 3 detects point cloud data. Therefore, although the measured water level is discrete, the LIDAR 3 has a short period (e.g., 100 [ms]), so graph 69B is close to a continuous line. Solid circles 68a to 68e show the predicted water level at the water level prediction point where the ship passed. Furthermore, the arrows corresponding to the solid circles 68a to 68e respectively indicate the width corresponding to the water level difference value.

[0150] 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 ship has passed. In the example of Figure 23(A), the predicted water level correction unit 18B calculates water level difference values ​​at five points corresponding to solid circles 68a to 68e.

[0151] Figure 23(B) is an example of a distribution showing the frequency (i.e., frequency) of multiple water level difference values ​​calculated within the immediately preceding specified 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 passability determination unit 16B as water level correction information required for correcting the bridge predicted water level. Thereafter, the bridge passability determination unit 16B corrects the predicted water level at each water level prediction point on the navigation route based on the water level correction information received from the predicted water level correction unit 18B, and calculates the estimated passage time and bridge corrected water level at each bridge passing point on the navigation route that the ship 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 possibility determination unit 16B then calculates the vessel's highest point height based on the water surface distance, the bridge corrected water level, and the highest point information IH, and calculates the predicted interval from the vessel's highest point height. Preferably, the bridge passage possibility determination unit 16B stores in the memory 12 or the like information (see Figure 15) that associates the calculated estimated passage time, bridge corrected water level, and predicted interval for each bridge that exists on the navigation route that the vessel has not yet passed.

[0152] (3-4) Functional Blocks 24 shows an example of a functional block of the predicted water level correction unit 18B related to the generation of water level correction information in Example 3. The predicted water level correction unit 18B functionally 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.

[0153] 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 measuring 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 less frequently than the frequency of self-position estimation by the self-position estimation unit 19B.

[0154] The measured water level calculation unit 85B calculates the measured 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 measured water level calculation unit 85B may calculate the measured water level each time a self-position estimation result is obtained, or may calculate the measured water level less frequently than the frequency at which a self-position estimation result is obtained.

[0155] The water level correction information generating unit 86B compares the predicted water level at the water level prediction point that the ship has passed with the measured water level based on the measured water level calculated by the measured water level calculating unit 85B and the predicted water level information D1, and calculates the water level difference value.The water level correction information generating unit 86B then generates water level correction information including the average value of the water level difference value at the water level prediction point that the ship has passed and other statistics, and supplies the generated water level correction information to the bridge passage possibility determining unit 16B.

[0156] (3-5) Processing flow FIG. 25 is an example of a flowchart showing the procedure of the water level correction information generation process executed by the predicted water level correction unit 18B in the third embodiment.

[0157] First, the predicted water level correction unit 18B acquires the ship reference height based on the result of self-position estimation 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 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 S32). The predicted water level correction unit 18B repeatedly executes the processes of steps S31 and S32 until the ship passes the water level prediction point.

[0158] Then, when the ship 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 a plurality of water level difference values ​​(i.e., the water level difference value at the water level prediction point immediately after passing, and the water level difference value at the water level prediction point passed before the water level prediction point).

[0159] (3-6) Variations The voxel data VD is not limited to a data structure including a mean vector and a covariance matrix as shown in FIG. 20. For example, the voxel data VD may directly include 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 point cloud data of the lidar 3 based on ICP (Iterative Closest Point).

[0160] As described above, the controller 13 of the information processing device 1B according to the third embodiment calculates the ship 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 water surface height, based on the water surface measurement data, which is the measurement data of the water surface measured by the LIDAR 3, and the ship reference height. The controller 13 then generates water level 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 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 is possible to accurately estimate the water level at bridge points on a candidate route. As a result, when the information processing device 1B performs highly accurate self-position estimation in the vertical direction, it can generate water level correction information that corrects the predicted water level with high accuracy using the self-position estimation results, and can accurately determine whether a ship can pass under an unpassed bridge that exists on the predicted water level navigation route.

[0161] <Fourth Example> In the fourth embodiment, the server device 7 collects the water level difference values ​​calculated based on the second or third embodiment, and updates the predicted water level DB 70 based on the collected water level difference values. As a result, the server device 7 preferably distributes to the information processing device 1 predicted water level information D1 indicating a highly accurate water level corrected based on the measured water level.

[0162] (4-1) composition Figure 26 shows the schematic configuration of an 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 execute the processing according to either the second or third embodiment. The server device 7C according to the fourth embodiment receives measured water level information "D2" relating 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. Hereinafter, the same components as those in the first, second, or third embodiment will be appropriately designated by the same reference numerals, and their description will be omitted.

[0163] 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 FIG. 11 or the information processing device 1B according to the third embodiment shown in FIG. 18. The information processing device 1C moves with a ship on a river and provides navigation support for the ship on which the information processing device 1 is installed based on the outputs of various sensors included in a sensor group 2 (not shown) and predicted water level information D1 transmitted from a server device 7C. When high-accuracy self-location estimation including the height direction is not performed, the information processing device 1C calculates a water level difference value based on the second embodiment. When high-accuracy self-location estimation including the height direction (e.g., self-location estimation based on NDT scan matching) is performed, the information processing device 1C calculates a water level difference value based on the third embodiment. The information processing device 1C transmits measured water level information D2 to the server device 7C, the measured water level information D2 including the calculated water level difference value, location information indicating the location 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.

[0164] A server device 7C according to the fourth embodiment updates a predicted water level DB 70 based on measured water level information D2 received from an information processing device 1C. FIG. 27 is a block diagram showing an example of the hardware configuration of the server device 7C. The server device 7C mainly has an interface 71, a memory 72, and a controller 73. These elements are interconnected via a bus line.

[0165] The interface 71 performs interface operations related to the exchange of data between the server device 7C and external devices. In this embodiment, the interface 71 performs processing to send predicted water level information D1 to the information processing device 1C and processing to receive measured water level information D2 from the information processing device 1C under the control of the controller 73. The memory 72 is composed of various volatile and non-volatile memories such as RAM, ROM, a hard disk drive, and flash memory. The memory 72 stores programs that the controller 73 uses to execute predetermined processes. The memory 72 also stores the predicted water level DB 70 described in the first embodiment and a measured water level information DB 75, which is a database that accumulates measured water level information D2. The predicted water level DB 70 is updated based on the measured water level information DB 75, as described below.

[0166] (4-2) Data Structure 28(A) is an example of the data structure of the predicted water level DB 70. The predicted water level DB 70 mainly has the following items: "water level predicted location," "update time," and "predicted water level."

[0167] "Water level prediction location" is information that identifies the target water level prediction location in each record of the predicted water level DB70. Here, as an example, the two-dimensional coordinate values ​​(e.g., a pair of latitude and longitude) of the water level prediction location are recorded as the "water level prediction location." "Update date and time" indicates the date and time when the predicted water level was updated. Note that if the predicted water level has not been updated, "Update date and time" indicates the predicted date and time of the predicted water level. "Update date and time" may be set for each predicted water level for each time. "Predicted water level" indicates the predicted water level for each time 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.

[0168] 28(B) is an example of the data structure of the measured water level information D2. The measured water level information D2 mainly has the following items: "location information," "date and time," "water level difference value," "water level difference standard deviation," "predicted water level," and "measured water level."

[0169] "Location information" is location information that indicates 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 expressed by two-dimensional coordinate values ​​that indicate the above-mentioned location on a horizontal plane. "Date and time" indicates the date and time (time) when the water level difference value (or measured water level) was calculated. "Water level difference value" indicates the average value of the water level difference. "Water level difference standard deviation" indicates the standard deviation of the water level difference. "Predicted water level" indicates the predicted water level used to calculate the corresponding water level difference value. Note that "predicted water level" may include information on the date and time when the predicted water level was predicted or updated (update date and time) in addition to or instead of the predicted water level. For example, if the predicted water level information D1 received by the information processing device 1C includes the update date and time information shown in Figure 28(A), this information on time indicates the update date and time. "Measured water level" indicates the measured water level used to calculate the corresponding water level difference value.

[0170] The data structure of the measured water level information D2 is not limited to the data structure shown in the figure. For example, the measured water level information D2 may not include information about the "predicted water level" and the "measured water level." In another example, the measured water level information D2 may include only one record instead of including multiple records. In this case, the information processing device 1C transmits the measured water level information D2 including a record related to the calculated water level difference value to the server device 7C, for example, every time the information processing device 1C calculates a water level difference value.

[0171] (4-3) Forecast water level update process Next, the process of updating the predicted water level recorded in the predicted water level DB 70 (predicted water level update process) will be specifically described.

[0172] 29 is an example of a functional block of the controller 73 of the server device 7C according to Example 4. As shown in the figure, the controller 73 of the server device 7C according to Example 4 functionally includes a receiving unit 76, an updating unit 77, and a distribution unit 78.

[0173] The receiving unit 76 receives the measured water level information D2 from each information processing device 1C via the interface 71. Then, the receiving unit 76 stores the received measured water level information D2 in the measured water level information DB75.

[0174] 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 water level prediction point indicated by the location information (i.e., the water level prediction 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 the measured water level information D2 shown in FIG. 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 water level prediction point, and corrects the predicted water level of the water level prediction 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 water level prediction point. In this case, the update unit 77 performs statistical processing on the water level difference values ​​received from multiple information processing devices for each water level prediction point, and adds a representative value such as the average value to the predicted water level at each time of the corresponding water level prediction point.

[0175] In addition, when performing statistical processing on the water level difference values ​​received from the above-mentioned multiple information processing devices, the update unit 77 may perform weighted averaging processing using the standard deviation of the water level difference. For example, when the water level difference values ​​μ i and the standard deviation of water level difference σ i (i=1, 2, . . . , N), the update unit 77 calculates a correction value "Cw" for the predicted water level using the following equation (7).

[0176]

number

[0177] 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 updating the first record in FIG. 28(A), the update unit 77 extracts from the measured water level information DB 75 records whose "location information" indicates a location within a predetermined distance (a threshold for determining whether the locations are the same) from the water level prediction point "(va, wa)" and whose "date and time" is after "September 12, 17:00," 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 the water level difference value whose update date and time match the update date and time recorded in the predicted water level DB 70. This effectively suppresses correction of the predicted water level using a water level difference value calculated based on old predicted water level information.

[0178] The distribution unit 78 transmits predicted water level information D1, which includes information related to the predicted water level 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 related to the predicted water level of a water level prediction point in an area or river specified by the request information to the information processing device 1C, or may transmit predicted water level information D1 related to the updated predicted water level to the information processing device 1C every time the predicted water level DB 70 is updated. In this way, the distribution unit 78 distributes information related to the predicted water level to the information processing device 1C based on either a push-type information distribution method or a pull-type information distribution method.

[0179] 30(A) is an example of a flowchart executed by an information processing device 1C according to Example 4. The information processing device 1C repeatedly executes the processing of this flowchart while the ship is in operation.

[0180] The information processing device 1C receives the 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 ship has passed, 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 flowchart of the water level correction information generation process shown in Figure 17 or Figure 25. Then, the information processing device 1C determines whether it is time to transmit measured water level information D2 (step S43). Note that the information processing device 1C may, for example, collectively transmit records corresponding to the calculated water level difference values ​​to the server device 7C as measured water level information D2 each time it calculates a predetermined number of water level calculation values, or may collectively transmit records corresponding to all the calculated water level difference values ​​to the server device 7C as measured water level information D2 after completing navigation of the navigation route.

[0181] If it is time to send the measured water level information D2 (step S43; Yes), the information processing device 1C sends the measured water level information D2, which includes the water level difference value calculated in step S42 and a set of the corresponding date and time information and position information, to the server device 7C (step S44).On the other hand, if the information processing device 1C determines that it is not time to send the measured water level information D2 (step S43; No), and if 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 the water level prediction point in step S42.

[0182] 30(B) is an example of a flowchart executed by the server device 7C according to Example 3. The server device 7C repeatedly executes the process of this flowchart.

[0183] 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 it is time to update the predicted water level DB 70 (step S52). For example, if there is a water level prediction point where a predetermined number of water level difference values ​​or more have been collected, the server device 7C may determine that it is time to update the predicted water level of the water level prediction point. 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 of the water level prediction point where a water level difference value has been collected. Then, 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.

[0184] On the other hand, if the server device 7C determines that it is time to update the predicted water level DB70 (step S52; Yes), it calculates an updated value of the predicted water level based on the water level difference value registered in the measured water level information DB75 (step S53). In this case, the server device 7C calculates an updated value of the predicted water level 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. The server device 7C then updates the predicted water level DB70 based on the calculated updated value of the predicted water level (step S54). Thereafter, the server device 7C distributes predicted water level information D1 based on the updated predicted water level DB70 to each information processing device 1C. This allows the server device 7C to distribute predicted water level information D1 indicating accurate predicted water levels that accurately reflect the water levels measured at each water level prediction point to the information processing device 1C.

[0185] (4-4) Variations The server device 7C may calculate an updated value of the predicted water level based on the measured water level included in the measured water level information D2, instead of calculating an updated value of the predicted water level based on the water level difference value included in the measured water level information D2.

[0186] In this modification, 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 at which the measured water level was calculated. The server device 7C then calculates a water level difference value by referring to the predicted water level DB 70 and the measured water level information DB 75 that reflects the measured water level information D2, and calculates an update value for the predicted water level based on the calculated water level difference value in accordance with the explanation of the fourth embodiment above. In this case, the server device 7C refers to the measured water level information DB 75 and calculates a representative value, such as the average value of the water level measurement values, for each water level prediction point and time period (the 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, for each water level prediction point and time period, the difference value between the predicted water level registered in the predicted water level DB 70 and a representative value, such as the average value of the corresponding water level measurement values, as the water level difference value. Then, the server device 7C calculates a representative value such as the average value of the calculated water level difference values ​​for each water level prediction point, and calculates an updated value of the predicted water level at each water level prediction point based on the calculated water level difference values.

[0187] In this modified example, the server device 7C can also update each predicted water level recorded in the predicted water level DB70 to an accurate value that accurately reflects the water level measured at each water level prediction point, and deliver predicted water level information D1 indicating the accurate predicted water level to the information processing device 1C.

[0188] As described above, the controller 73 of the server device 7C according to the fourth embodiment stores the predicted water level DB70, which indicates the predicted water level for each water level prediction point in a river. The controller 73 then receives, from multiple vessels, measured water level information D2 relating to the measured water levels measured by the vessels. The controller 73 then updates the predicted water level DB70 based on the measured water level information D2. The controller 73 then distributes the predicted water level information D1 based on the predicted water level DB70. In this manner, the server device 7C can distribute, to the information processing device 1C, the predicted water level information D1 indicating accurate predicted water levels that accurately reflect the water levels measured at each water level prediction point.

[0189] In the above-described embodiments, the program can be stored using various types of non-transitory computer-readable media and supplied to a controller or the like that is a computer. 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 memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)).

[0190] Although the present invention has been described above with reference to the examples, the present invention is not limited to the above examples. Various modifications within the scope of the present invention that would be understood by those skilled in the art can be made to the configuration and details of the present invention. In other words, the present invention naturally includes various modifications and alterations that would be possible for those skilled in the art in accordance with the entire disclosure, including the claims, and the technical ideas. Furthermore, the disclosures of the above-cited patent documents and other documents are incorporated herein by reference. [Explanation of symbols]

[0191] 1, 1A, 1B, 1C Information processing equipment 2 Sensor group 3 Rider 5 GPS receiver 7, 7C server device 10 River Map DB

Claims

[Claim 1] a predicted water level information acquisition means for acquiring predicted water level information relating to predicted water levels at water level prediction points where water levels are predicted on one or more candidate routes that are candidates for the ship's navigation route; a bridge predicted water level calculation means for calculating, based on the predicted water level information, a bridge predicted water level, which is a predicted water level at the scheduled time of passage of the vessel at each bridge passing point, which is a point where the vessel will pass a bridge existing on each of the candidate routes; a determining means for determining whether each of the candidate routes is suitable as the navigation route based on the predicted bridge water level; An information processing device having the above.

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