Information processor, determination method, program, and storage medium

Abstract

To provide an information processor capable of accurately calculating height of the highest point of a vessel even when a highly accurate self-position estimation result in the hight direction cannot be obtained.SOLUTION: A controller 13 of an information processor 1 obtains measurement data of a feature and measurement data on a water surface measured by a rider 3 provided on a vessel. In addition, the controller 13 acquires information regarding height of the feature from a river map DB 10. Then, the controller 13 acquires highest point information IH regarding the height from a reference position of the vessel to the highest point of the vessel. Then, the controller 13 calculates a vessel highest point height based on the feature measurement data, the water surface measurement data, the information regarding the height of the feature, and the highest point information IH.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present disclosure relates to determining whether a ship can pass under a bridge. [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 determining a navigation route, it is common to select a route that allows safe passage under a bridge based on tide forecast information. However, whether a ship can actually pass under a bridge must be confirmed before approaching the bridge, taking into account the possibility that the water level may be higher than the tide forecast. If it is determined in advance that passing under a bridge is risky, it can take measures such as turning back or changing the route when the river is wide and there are no other ships. Thus, determining whether a ship can pass under a bridge in advance is important for safe navigation. Furthermore, when determining whether a ship can pass under a bridge, it is necessary to accurately recognize the height of the ship's highest point. On the other hand, ships without a self-localization system must calculate the height of the ship's highest point without relying on the self-localization results. This also applies to ships that only estimate their self-localization in planar coordinates, or ships that estimate their self-localization in vertical direction but with low estimation accuracy.

[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 calculate the height of the highest point of a ship even when highly accurate self-position estimation results in the vertical direction cannot be obtained. [Means for solving the problem]

[0006] The claimed invention is a first acquisition means for acquiring feature measurement data, which is measurement data of features measured by a measurement device installed on the ship, and water surface measurement data, which is measurement data of the water surface measured by the measurement device; a second acquisition means for acquiring information about the height of the feature from map data; a vessel highest point height calculation means for calculating the vessel highest point height, which is the height of the highest point, based on the feature measurement data, the water surface measurement data, information on the height of the feature, and highest point information on the height from the reference position of the vessel to the highest point of the vessel; The information processing device has the following.

[0007] The claimed invention also includes: A computer-implemented control method comprising: Acquire feature measurement data, which is measurement data of features measured by a measuring device installed on the ship, and water surface measurement data, which is measurement data of the water surface measured by the measuring device; obtaining information about the height of the feature from map data; Calculating the ship's highest point height, which is the height of the highest point, based on the feature measurement data, the water surface measurement data, information on the height of the feature, and highest point information on the height from the reference position of the ship to the highest point of the ship. It is a control method.

[0008] The claimed invention also includes: Acquire feature measurement data, which is measurement data of features measured by a measuring device installed on the ship, and water surface measurement data, which is measurement data of the water surface measured by the measuring device; obtaining information about the height of the feature from map data; This is a program that causes a computer to execute a process of calculating the ship's highest point height, which is the height of the highest point, based on the feature measurement data, the water surface measurement data, information regarding the height of the feature, and highest point information regarding the height from the ship's reference position to the ship's highest point. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a schematic configuration diagram of an operation support system. [Figure 2] FIG. 2 is a block diagram showing a functional configuration of the information processing device. [Figure 3] FIG. 10 is a diagram showing an outline of a process for determining whether or not a bridge can be passed; [Figure 4] (A) shows the frequency distribution of the water surface distance. (B) shows an example of a graph of the time transition of the standard deviation of the water surface distance. [Figure 5] 10 is an example of a functional block of a bridge passability determination unit. [Figure 6] 10 is an example of a flowchart of a process for determining whether or not a bridge can be passed; [Figure 7]This is a view from behind of a ship passing a bridge for which the predicted distance between the ship and the bridge has been calculated. [Figure 8] The frequency distribution of the difference between the predicted interval and the actual measurement interval is shown. [Figure 9] FIG. 10 is a diagram showing an outline of a process for determining whether or not a bridge can be passed through in a modified example. [Figure 10] 10 is an example of a flowchart illustrating a procedure for determining whether or not a bridge can be passed through in a modified example. DETAILED DESCRIPTION OF THE INVENTION

[0010] According to a preferred embodiment of the present invention, an information processing device includes a first acquisition means for acquiring feature measurement data, which is measurement data of features measured by a measurement device installed on a ship, and water surface measurement data, which is measurement data of the water surface measured by the measurement device, a second acquisition means for acquiring information on the height of the feature from map data, and a ship highest point height calculation means for calculating the ship highest point height, which is the height of the highest point, based on the feature measurement data, the water surface measurement data, the information on the height of the feature, and highest point information on the height from the reference position of the ship to the highest point of the ship. According to this aspect, the information processing device can accurately calculate the height of the highest point of the ship without using the self-location estimation result.

[0011] In one aspect of the information processing device, the information processing device further has a feature distance calculation means for calculating a feature distance, which is the distance in the height direction from the reference position to the feature, based on the feature measurement data, a water surface distance calculation means for calculating a water surface distance, which is the distance in the height direction from the reference position to the water surface, based on the water surface measurement data, and a water surface height calculation means for calculating a water surface height, which is the height of the water surface, based on the height of the feature, the feature distance, and the water surface distance, and the ship highest point height calculation means calculates the ship highest point height based on the water surface height, the water surface distance, and the height from the reference position of the ship to the highest point of the ship. According to this aspect, the information processing device calculates the water surface height using the water surface measurement data and the feature measurement data, and can accurately calculate the ship highest point height based on the calculated water surface height.

[0012] In another aspect of the information processing device, the information processing device further includes a prediction interval calculation means for calculating a prediction interval, which is a predicted distance between the bridge and the ship, based on a bridge height, which is the height of the bridge based on the map data, and the ship's highest point height, and a passability determination means for determining whether the ship can pass under the bridge based on the prediction interval. According to this aspect, the information processing device can accurately determine whether the ship can pass under the bridge using the calculated ship's highest point height.

[0013] In another aspect of the information processing device, the information processing device further includes a threshold determination means for determining a threshold to be compared with the prediction interval in the passability determination, the threshold determination means determining the threshold based on an index representing the variation in water surface distance, which is the vertical distance from the reference position to the water surface. According to this aspect, the information processing device can set a threshold taking wave height into consideration and accurately determine whether the ship can pass under the bridge. In a preferred example, the threshold determination means determines the threshold based on the maximum value of multiple standard deviations calculated over a predetermined period.

[0014] In another aspect of the information processing device, the threshold value determining means calculates the measured interval between the bridge and the ship based on measurement data obtained by measuring the bridge with a measuring device when the ship passes over the bridge, calculates the difference between the measured interval and the predicted interval for multiple bridges, and determines the threshold value based on an index representing the variability of the calculated difference value and an index representing the variability of the water surface distance. This aspect makes it possible to determine the threshold value to be compared with the predicted interval by accurately considering the variability of the deviation of the predicted interval from the measured interval. Preferably, the threshold value determining means increases the threshold value as the variability indicated by the index increases.

[0015] In another aspect of the information processing device, the prediction interval calculation means calculates the actual measured interval between the first bridge and the ship based on measurement data obtained by the measurement device measuring the first bridge when the ship passes over the first bridge, and generates correction information for correcting the predicted interval for a second bridge other than the first bridge based on the actual measured interval and the predicted interval for the first bridge. In other words, the above-mentioned correction information is correction information that can correct the predicted interval in a general manner (i.e., without limiting the bridge) rather than depending on a specific bridge. With this aspect, the information processing device can obtain correction information for highly accurately correcting the predicted interval for each bridge used to determine whether the ship can pass under the bridge.

[0016] In another aspect of the information processing device, the feature is a bridge, and the passability determination means makes the passability determination based on the height of the bridge acquired from the map data by the second acquisition means and the prediction interval. According to this aspect, when a bridge is present within a measurement range of a measurement device, the information processing device can accurately determine whether a ship can pass under a bridge based on map data including measurement data of the bridge and information about the bridge height.

[0017] In another aspect of the information processing device, the feature is a feature different from the bridge, the second acquisition means further acquires information about the height of the bridge from the map data, and the passability determination means makes the passability determination based on the height of the bridge acquired from the map data by the second acquisition means and the prediction interval. According to this aspect, the information processing device calculates the highest point height of the ship based on a feature whose height information is registered in the map data, and by referring to the information about the bridge height from the map data, it is possible to accurately determine whether the ship can pass under the bridge.

[0018] According to another preferred embodiment of the present invention, there is provided a control method executed by a computer, which acquires feature measurement data, which is measurement data of features measured by a measuring device installed on a ship, and water surface measurement data, which is measurement data of the water surface measured by the measuring device, acquires information about the height of the feature from map data, and calculates a ship highest point height, which is the height of the highest point, based on the feature measurement data, the water surface measurement data, the information about the feature height, and highest point information related to the height from a reference position of the ship to the highest point of the ship. By executing this determination method, the computer can accurately calculate the height of the ship's highest point without using the result of self-location estimation.

[0019] According to yet another preferred embodiment of the present invention, a computer acquires feature measurement data, which is measurement data of features measured by a measuring device installed on a ship, and water surface measurement data, which is measurement data of the water surface measured by the measuring device, acquires information about the height of the feature from map data, and executes a process of calculating a ship's highest point height, which is the height of the highest point, based on the feature measurement data, the water surface measurement data, the information about the feature height, and highest point information about the height from the ship's reference position to the highest point. By executing this program, the computer can accurately calculate the height of the ship's highest point without using the self-location estimation result. Preferably, the program is stored in a storage medium. [Example]

[0020] Preferred embodiments of the present invention will now be described with reference to the drawings.

[0021] (1) Overview of the flight support system Figures 1(A) to 1(C) show a schematic configuration of a navigation assistance system according to this embodiment. Specifically, Figure 1(A) shows a block diagram of the navigation assistance system, Figure 1(B) 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 1(C) is a rear view of the ship and the field of view 90 of the lidar 3. The navigation assistance system has an information processing device 1 that moves with the ship, which is a mobile object, and a group of sensors 2 mounted on the ship.

[0022] 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. In this embodiment, as an example of navigation support, the information processing device 1 determines whether a ship can pass under a bridge that the ship is scheduled to pass through and executes processing according to the determination result. 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. Furthermore, the information processing device 1 does not perform high-precision self-location estimation, including estimation of the ship's vertical position. In other words, the ship is not equipped with a self-location estimation system that estimates the ship's vertical position with high precision. Note that the information processing device 1 acquires the ship's position on the water surface, which is necessary when referencing a river map database described later, from a GPS (Global Positioning Satellite) receiver 5 described later or the like.

[0023] 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 receiver 5. Note that instead of the GPS receiver 5, the sensor group 2 may include a receiver that generates positioning results of a GNSS other than GPS.

[0024] The LIDAR 3 is an external sensor that emits a pulsed laser beam within a predetermined angular range in the horizontal and vertical directions (i.e., elevation and depression angles) to discretely measure the distance to an object in the external world and generate three-dimensional point cloud data indicating the position of the object. In this case, 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. Data measured for each direction of laser light irradiation (scanning position) 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 beam within the field of view of a two-dimensional array sensor. The LIDAR 3 is an example of a "measurement device" in the present invention.

[0025] 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.

[0026] (2) Configuration of information processing device 2 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 data 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 drive source such as an engine or an electric motor, a screw that generates a forward thrust based on the drive force of the drive source, a thruster that generates a lateral thrust based on the drive force of the drive 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 a control signal generated by the controller 13 to each of these components. Note that, 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 a hardware interface for connecting to an external device via a cable or the like. 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 data (also referred to as "landmark data") on 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 its size. Note that the attribute information of the feature data corresponding to a bridge includes at least information regarding 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 navigation route information regarding the navigation route that a 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] Furthermore, functionally, the controller 13 has a bridge passage possibility determination unit 16. The bridge passage possibility determination unit 16 determines whether the ship can pass under a bridge that exists on the navigation route that the ship is scheduled to pass, based on feature data related to the bridge, the highest point information IH, and the point cloud data output by the LIDAR 3. The controller 13 functions as a "first acquisition means," a "second acquisition means," a "feature distance calculation means," a "water surface distance calculation means," a "water surface height calculation means," a "ship's highest point height calculation means," a "prediction interval calculation means," a "passage possibility determination means," a "threshold value determination means," and a computer that executes a program, etc.

[0036] (3) Bridge passability determination process Next, the process executed by the bridge passage possibility determination unit 16 to determine whether or not a ship can pass under a bridge (also referred to as the "bridge passage possibility determination process") will be described. Briefly, the bridge passage possibility determination unit 16 calculates the height of the ship's highest point (also referred to as the "ship's highest point height") based on information about the bridge height (also referred to as the "bridge height") based on the river map DB 10, point cloud data obtained by measuring the bridge and water surface with the LIDAR 3, and the highest point information IH. The bridge passage possibility determination unit 16 then predicts the vertical distance between the ship and the bridge under the bridge (also referred to as the "prediction interval") based on the bridge height and the ship's highest point height, and determines whether or not the ship can pass under the bridge based on this prediction interval. This allows the bridge passage possibility determination unit 16 to reconfirm in advance whether or not a ship can safely pass under a bridge, even if the ship is not equipped with a self-localization system that estimates its position in the vertical direction with high accuracy.

[0037] The bridge height represents the height of the bridge's base above the river (i.e., the height of the lower part of the girder). The "height" used in calculating the vessel's highest point height and bridge height represents the height (e.g., elevation) in the coordinate system used in the river map DB10.

[0038] FIG. 3 is a diagram showing an overview of the bridge passage possibility determination process. FIG. 3 shows a ship passing over a bridge 30, observed from behind. In the example of FIG. 3, the ship is equipped with two lidars 3, 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 (bridge height and ship's highest point height), line L2 indicates the water surface, 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, measured points "m1" to "m8" indicate positions measured by the lidars 3.

[0039] The bridge passage possibility determination unit 16 calculates a predicted interval to be used for determining whether the vessel is able to pass through the bridge when it determines that the vessel is approaching the bridge 30. In this case, the bridge passage possibility determination unit 16 calculates a predicted interval to be used for determining whether the vessel is able to pass through the bridge by specifying the heights or widths corresponding to the arrows A1 to A7 in the order of the arrows A1 to A7, as will be described below.

[0040] First, the bridge passage possibility determination unit 16 extracts feature data corresponding to the bridge 30 over which the vessel is scheduled to pass from the river map DB 10, and by referring to the extracted feature data, identifies the bridge height (see arrow A1) of the bridge 30. In this case, the bridge passage possibility determination unit 16, for example, identifies the bridge 30 over which the vessel will next pass based on the vessel's current position and navigation route from among the bridges registered in the river map DB 10, and identifies the bridge height of the bridge 30.

[0041] Furthermore, the bridge passage possibility determination unit 16 calculates the height distance (the distance corresponding to arrow A2, also referred to as the "underbridge distance") from the ship reference position to the bottom of the girder 32, which is the bottom surface of the bridge 30, based on point cloud data (also referred to as "bridge measurement data") obtained by measuring the bridge 30 by the LIDAR 3. In this case, when the distance between the ship and the bridge 30 is within the maximum measurement distance of the LIDAR 3, the bridge passage possibility determination unit 16 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 bridge measurement data. At this time, in order to exclude points that have been detected as non-bridge locations 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 this maximum value. In Fig. 3, the bridge passability determination unit 16 regards data corresponding to measurement points "m1" to "m4" on the girder lower part 32 as bridge measurement data, and extracts it from the point cloud data generated by the LIDAR 3. Then, the bridge passability determination unit 16 calculates a representative value, such as the average value or minimum value of the coordinate values ​​in the height direction of the extracted bridge measurement data, as the distance under the bridge.

[0042] The bridge passage possibility 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 the point cloud data (also referred to as "water surface measurement data") of the LIDAR 3 that measured the water surface. In this case, the bridge passage possibility 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 that are not the water surface, such as bridge piers, quays, and other ships, the minimum value of the 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 that is closest to that minimum value. In Figure 3, the bridge passage possibility determination unit 16 considers the data corresponding to the measurement points "m5" to "m8" on the water surface to be water surface measurement data and extracts it from the point cloud data of the LIDAR 3. Then, the bridge passage possibility 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 bridge passage possibility determination unit 16 may execute the calculation of the water surface distance multiple times 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 3 shows only measurement points "m1" 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.

[0043] Next, the bridge passage possibility determination unit 16 calculates the water surface height (see arrow A4) by subtracting the distance under the bridge (see arrow A2) and the water surface distance (see arrow A3) from the bridge height (see arrow A1). The water surface height indicates a height expressed on the same scale (e.g., altitude) as the bridge height (and the highest point height of the ship, which will be described later).

[0044] Next, the bridge passage possibility determination unit 16 identifies the height width (see arrow A5) from the ship reference position to the highest point by referring to the highest point information IH from the memory 12. Then, the bridge passage possibility determination unit 16 calculates the ship's highest point height (see arrow A6) which corresponds to the height obtained by adding the water surface height (see arrow A4) to the water surface distance (see arrow A3) and the height width (see arrow A5) from the ship reference position to the highest point.

[0045] Then, the bridge passage possibility 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 A7).

[0046] Thereafter, the bridge passage possibility determination unit 16 determines that the ship can pass through the bridge if the calculated prediction interval is equal to or greater than a threshold value (also called the "prediction interval threshold Th"), and determines that there is a risk that the ship will not be able to pass through the bridge safely if the prediction interval is less than the prediction interval threshold Th.

[0047] In this way, even if the bridge passage possibility determination unit 16 does not perform high-precision self-position estimation such as position estimation based on NDT scan matching, it can accurately determine whether or not the ship can pass over a bridge that it is scheduled to pass over, based on the point cloud data output by the lidar 3 and map data related to the bridge.

[0048] Next, a method for determining the prediction interval threshold Th will be described. As an example, a method for determining the prediction interval threshold Th based on an index representing the variation in the calculated water surface distance (see arrow A3) will be described. Note that instead of being determined based on the method described below, the prediction interval threshold Th may be set to a fixed value stored in advance in memory 12 or the like.

[0049] The bridge passability determination unit 16 calculates the standard deviation "σ1" of the multiple calculated values ​​of water surface distances calculated within the most recent predetermined period. For example, if the water surface distance is calculated at a cycle of 100 [ms], setting the predetermined period to 5 [s] will result in the standard deviation σ1 of 50 calculated water surface distance values. The standard deviation σ1 is an example of an "index representing the variation in water surface distance." Then, the bridge passability determination unit 16 calculates the standard deviation σ1 at every predetermined time interval, and multiplies the maximum value "σ1(max)" of the multiple calculated standard deviations σ1 by a predetermined coefficient "k" to calculate the prediction interval threshold Th. That is, the bridge passability determination unit 16 calculates the prediction interval threshold Th based on the following equation (1): Th=k·σ1(max) (1)

[0050] In this case, the coefficient k is set to a fixed value (for example, k=5) that provides a sufficient confidence interval.

[0051] FIG. 4(A) shows the frequency distribution of the water surface distance when the water surface distance (more specifically, the height direction value of each data point of the water surface measurement data) used to calculate the standard deviation σ1 is considered to be a discrete value. FIG. 4(B) shows an example of a graph of the time transition of the standard deviation σ1 calculated at predetermined time intervals. In FIG. 4(A), "μ1" represents the average water surface distance. The bridge passability determination unit 16 calculates the standard deviation σ1 by aggregating the height direction values ​​of each data point of the water surface measurement data, and further identifies the maximum value σ1(max) by monitoring the multiple standard deviations σ1 calculated at predetermined time intervals for a predetermined length of time as shown in FIG. 4(B).

[0052] Here, we will provide additional explanation about the effect of determining the prediction interval threshold Th based on the calculation results of the water surface distance. The variability in the water surface distance obtained as a result of measurements by the LIDAR 3 relative to the water surface is influenced by errors in the point cloud data output by the LIDAR 3 and the rocking of the ship, but is also caused by large wave height. When wave height is large, the vertical movement of the ship also becomes large, and the variability in the water surface measurement data also becomes large. Taking the above into consideration, the bridge passage possibility determination unit 16 increases the prediction interval threshold Th based on equation (1) as the standard deviation σ1 increases. This makes it possible to more accurately and safely determine whether or not to pass over the bridge.

[0053] 5 is an example of a functional block of the bridge passage possibility determination unit 16. Functionally, the bridge passage possibility determination unit 16 has a bridge height acquisition unit 61, an under-bridge distance calculation unit 62, a water surface distance calculation unit 63, a water surface height calculation unit 64, a ship's highest point height calculation unit 65, a prediction interval calculation unit 66, a threshold value determination unit 67, and a passage possibility determination unit 68.

[0054] The bridge height acquisition unit 61 extracts feature data corresponding to the bridge to be crossed from the river map DB 10 and acquires the bridge height based on the extracted feature data. The under-bridge distance calculation unit 62 calculates the under-bridge distance based on bridge measurement data measuring the bridge (i.e., above the horizontal direction). 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).

[0055] The water surface height calculation unit 64 calculates the water surface height based on the bridge height acquired by the bridge height acquisition unit 61, the under-bridge distance calculated by the under-bridge distance calculation unit 62, and the water surface distance calculated by the water surface distance calculation unit 63. Then, the ship's highest point height calculation unit 65 calculates the ship's highest point height based on the water surface distance calculated by the water surface distance calculation unit 63, the water surface height calculated by the water surface height calculation unit 64, and the heightwise width from the ship's reference position to the highest point indicated by the highest point information IH.

[0056] The prediction interval calculation unit 66 calculates the prediction interval based on the bridge height calculated by the bridge height acquisition unit 61 and the ship's highest point height calculated by the ship's highest point height calculation unit 65. The threshold determination unit 67 calculates the standard deviation σ1 and maximum value σ1(max) based on the water surface reflection data acquired by the water surface distance calculation unit 63, and determines the prediction interval threshold value Th by referring to equation (1).

[0057] The passage possibility determination unit 68 determines whether the bridge is passable or not based on the prediction interval calculated by the prediction interval calculation unit 66 and the prediction interval threshold Th determined by the threshold determination unit 67. Then, the passage possibility determination unit 68 supplies the determination result of whether the bridge is passable or not to other processing blocks, etc. of the controller 13. Thereafter, the controller 13 may perform display and / or audio output control, etc., based on the determination result of whether the bridge is passable or not, via the interface 11. For example, if the prediction interval is less than the prediction interval threshold, the controller 13 outputs a warning that the ship may not be able to pass the bridge safely. In another example, the controller 13 searches for an alternative route that does not pass over the bridge that has been determined to be unpassable, and outputs guidance information and performs navigation control for the new route that has been found.

[0058] 6 is an example of a flowchart of a bridge passability determination process executed by the bridge passability determination unit 16. The bridge passability determination unit 16 executes the process of the flowchart in order for each bridge that exists on the operating route, for example, after the ship starts operating based on the operating route.

[0059] First, the bridge passability determination unit 16 extracts feature data corresponding to the bridge that is the target of the bridge passability determination process from the river map DB 10, and obtains the bridge height of the bridge based on the extracted feature data (step S11).

[0060] Next, when the target bridge is present within the measurement range of the LIDAR 3, the bridge passage possibility determination unit 16 calculates the distance under the bridge based on the bridge measurement data, which is point cloud data of the LIDAR 3 that measured above (step S12). The bridge passage possibility determination unit 16 executes the process of step S12 when it determines that the vessel has approached the target bridge within a predetermined distance (in other words, that the bridge has entered the measurement range of the LIDAR 3). The bridge passage possibility determination unit 16 also calculates the water surface distance based on the water surface measurement data, which is point cloud data of the LIDAR 3 that measured below (step S13).

[0061] Note that steps S11, S12, and S13 may be executed in any order. Furthermore, the bridge passage possibility determination unit 16 may execute the processing of step S13 at any timing regardless of the distance between the ship and the bridge. Furthermore, the bridge passage possibility determination unit 16 may execute step S13 multiple times and use a representative value, such as an average value of multiple calculation results of water surface distances, as the value of the water surface distance in subsequent processing.

[0062] Next, the bridge passage possibility determination unit 16 calculates the water surface height based on the bridge height acquired in step S11, the distance under the bridge calculated in step S12, and the water surface distance calculated in step S13 (step S14). Then, the bridge passage possibility determination unit 16 calculates the vessel's highest point height based on the water surface height calculated in step S14, the water surface distance calculated in step S13, and the highest point information IH (step S15). Then, the bridge passage possibility determination unit 16 calculates a prediction interval based on the bridge height acquired in step S11 and the vessel's highest point height calculated in step S15 (step S16). Then, the bridge passage possibility determination unit 16 determines whether the vessel can pass the target bridge based on the prediction interval calculated in step S16 (step S17). In this case, the bridge passability determination unit 16 preferably sets the prediction interval threshold Th, which is a determination threshold used for comparison with the prediction interval, based on the standard deviation σ1, which is an index representing the variation in the water surface distance calculated in step S13, as described above.

[0063] (4) Variations The following describes preferred modifications of the above-described embodiment. The following modifications may be applied to these embodiments in combination.

[0064] (Variation 1) The bridge passage possibility determination unit 16 may use an adaptively changing value as the coefficient k used to calculate the prediction interval threshold Th, instead of using a fixed value. Specifically, the bridge passage possibility determination unit 16 measures the actual measurement value of the heightwise distance between the ship and the bridge below the bridge (also referred to as the "measured distance"), and sets the coefficient k based on the degree of variation in the difference between the actual distance and the predicted distance. In this way, the bridge passage possibility determination unit 16 sets the prediction interval threshold Th to an appropriate value that is not larger than necessary and that ensures sufficient safety.

[0065] FIG. 7 is a view of a ship passing a bridge 30 for which a prediction interval has been calculated, observed from behind.

[0066] First, when passing over a bridge 30 for which the predicted interval has been calculated, the bridge passage possibility determination unit 16 calculates the distance under the bridge (see arrow A2) based on the bridge measurement data (data corresponding to measured points "ma1" to "ma4" in Figure 10), which is point cloud data from the lidar 3 that represents measurement results above the horizontal direction. At this time, in order to exclude points that detect bridge piers, the maximum z coordinate value of the point cloud data is found, and the z coordinate value of each measurement point is extracted so that the bridge measurement data can be obtained. Note that the bridge measurement data used by the bridge passage possibility determination unit 16 at this time is data generated later than the bridge measurement data used to calculate the predicted interval.

[0067] Next, the bridge passability determination unit 16 calculates the actual measured interval (see arrow A8) by subtracting the height (see arrow A5) of the highest point from the ship reference position based on the highest point information IH from the calculated distance under the bridge (see arrow A2).

[0068] The bridge passage possibility determination unit 16 then calculates the difference between the predicted interval and the measured interval by subtracting the measured interval from the predicted interval of the bridge 30. The bridge passage possibility determination unit 16 then calculates the above-mentioned difference values ​​for multiple bridges that the ship has passed through within a predetermined period, and calculates the average "μ2" and standard deviation "σ2" of the calculated difference values. Figure 8 shows the frequency distribution of the above-mentioned difference values ​​when they are considered to be discrete values.

[0069] Next, the bridge passage possibility determination unit 16 regards the average μ2 as a constant offset for the prediction interval (i.e., used universally for all bridges), and corrects the prediction interval by subtracting the average μ2 from the prediction interval for all bridges that the ship is scheduled to pass over. This improves the accuracy of the prediction interval for bridges that the ship is scheduled to pass over, and improves the accuracy of the bridge passage determination process. The above-mentioned offset is an example of "correction information." Furthermore, the bridge used to calculate the average μ2 and standard deviation σ2 is an example of a "first bridge," and the bridge whose prediction interval is corrected (i.e., the bridge that the ship is scheduled to pass over) is an example of a "second bridge."

[0070] Furthermore, the bridge passage possibility determination unit 16 sets the coefficient k in equation (1) so that the prediction interval threshold Th increases as the standard deviation σ2 increases. For example, the bridge passage possibility determination unit 16 sets the coefficient k to "3 + σ2" and determines the prediction interval threshold Th based on the following equation (2). Th=(3+σ2)·σ1(max) (2)

[0071] As described above, according to this modified example, the bridge passability determination unit 16 can improve the prediction accuracy of the prediction interval and adaptively set the prediction interval threshold value Th so that the prediction interval threshold value Th is larger when the prediction interval varies greatly from the actually measured interval, and the prediction interval threshold value Th is smaller when the variation is small.

[0072] (Variation 2) The bridge passability determination unit 16 may calculate the vessel's highest point height based on the measurement results by the lidar 3 of features other than bridges for which feature data exists in the river map DB 10, and the feature data of the features.

[0073] FIG. 9 is a diagram showing an overview of the bridge passage possibility determination process in Modification 2. FIG. 9 shows a rear view of a ship passing beside a feature 39 (e.g., a signboard) whose feature data is registered in the river map DB 10. Line "L11" indicates the origin position of the height (bridge height and ship's highest point height), line "L12" indicates the water surface position, and line "L13" indicates a position that is the same height as the ship's reference position. Line "L14" indicates a position that is the same height as the ship's highest point, and line "L15" indicates a position that is the same height as the feature 39 (here, the height of the center position of feature 39). Furthermore, measurement points "mb1" to "mb4" indicate the surface positions of feature 39 measured by LIDAR 3, and measurement points "mb5" to "mb8" indicate the water surface positions measured by LIDAR 3. In addition, there are measurement points corresponding to the surface positions of the supports of feature 39, etc. Then, the bridge passability determination unit 16 calculates the predicted interval to be used for determining whether the bridge is passable by identifying the heights or widths corresponding to the arrows "A11" to "A16" in the order of the arrows "A11" to "A16", as will be described below.

[0074] Fig. 10 is an example of a flowchart showing the procedure of the bridge passability determination process in Modification 2. Note that steps S23, S25, S26, and S27 in Fig. 10 are the same processes as steps S13, S15, S16, and S17 in Fig. 6, respectively. Hereinafter, the bridge passability determination process of the bridge passability determination unit 16 in Modification 2 will be described with reference to Figs. 9 and 10.

[0075] The bridge passability determination unit 16 extracts feature data corresponding to a feature 39 present within the measurement range of the LIDAR 3 from the river map DB 10, and obtains the height (also referred to as "feature height") of the feature 39 based on the extracted feature data (step S21). The feature height of the feature 39 is the height corresponding to the arrow A11 in Fig. 9, and here, as an example, the height of the center position of the feature 39 is registered in the feature data of the feature 39.

[0076] Next, the bridge passage possibility determination unit 16 calculates the height direction distance (also referred to as the "feature distance") from the ship reference position to the feature 39 based on point cloud data (also referred to as the "feature measurement data") obtained by measuring feature 39 present within the measurement range of the LIDAR 3 by the LIDAR 3 (step S22). The feature distance of the feature 39 is the distance corresponding to arrow A12 in FIG. 9. The bridge passage possibility determination unit 16 may use any method to perform the process of extracting feature measurement data of the feature 39 (data corresponding to measurement points mb1 to mb4) from the point cloud data of the LIDAR 3. For example, if the feature data of the feature 39 includes information on the reflectance of the feature 39, the bridge passage possibility determination unit 16 can obtain the feature measurement data by extracting data indicating the received light intensity corresponding to the reflectance of the feature 39 from the point cloud data of the LIDAR 3 based on the information on the reflectance. Furthermore, if size information is included, feature measurement data can be obtained by extracting from the point cloud data those that match that size.

[0077] The bridge passability determination unit 16 also calculates the water surface distance (see arrow A13) based on the water surface measurement data (data corresponding to measurement points mb5 to mb8) which is point cloud data of the lidar 3 that measures the below (step S23).

[0078] Next, the bridge passage possibility determination unit 16 calculates the water surface height (see arrow A14) based on the feature height acquired in step S21, the feature distance calculated in step S22, and the water surface distance calculated in step S23 (step S24).Then, the bridge passage possibility determination unit 16 calculates the ship's highest point height (see arrow A16) based on the water surface height calculated in step S24 (see arrow A14), the water surface distance calculated in step S23 (see arrow A13), and the width in the height direction between the ship's reference position and the highest point indicated by the highest point information IH (see arrow A15) (step S25).

[0079] Thereafter, the bridge passage possibility determination unit 16 calculates a prediction interval based on the bridge height of the bridge that is the target of the bridge passage possibility determination process and the vessel's highest point height (see arrow A16) calculated in step S25 (step S26). In this case, the bridge passage possibility determination unit 16 extracts feature data corresponding to the bridge that is the target of the bridge passage possibility determination process from the river map DB 10, and obtains the bridge height of the bridge based on the extracted feature data.

[0080] Then, the bridge passage possibility determination unit 16 determines whether the ship can pass the target bridge based on the prediction interval calculated in step S26 (step S27). In this case, the bridge passage possibility determination unit 16 preferably sets the prediction interval threshold Th, which is a determination threshold used for comparison with the prediction interval, based on the standard deviation σ1, which is an index representing the variation in the water surface distance calculated in step S23, as described above.

[0081] As described above, in this modified example, the bridge passability determination unit 16 can accurately perform the bridge passability determination process based on the measurement results of any feature registered in the river map DB 10 and the feature data for that feature and each bridge over which the ship is scheduled to pass.

[0082] As described above, the controller 13 of the information processing device 1 according to this embodiment acquires measurement data of features (for example, a bridge 30 or a feature 39) measured by the LIDAR 3 provided on the ship, and measurement data of the water surface. The controller 13 also acquires information related to the height of the features from the river map DB 10. The controller 13 then acquires highest point information IH related to the height from the ship's reference position to the highest point of the ship. The controller 13 then calculates the ship's highest point height based on the feature measurement data, the water surface measurement data, the information related to the feature height, and the highest point information IH. This allows the controller 13 to accurately calculate the ship's highest point height, which is necessary for determining whether the ship can pass over a bridge, without requiring a highly accurate self-position estimation result such as NDT scan matching.

[0083] 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)).

[0084] 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]

[0085] 1. Information processing equipment 2 Sensor group 3 Rider 5 GPS receiver 10 River Map DB

Claims

[Claim 1] a first acquisition means for acquiring feature measurement data, which is measurement data of features measured by a measurement device installed on the ship, and water surface measurement data, which is measurement data of the water surface measured by the measurement device; a second acquisition means for acquiring information about the height of the feature from map data; a vessel highest point height calculation means for calculating the vessel highest point height, which is the height of the highest point, based on the feature measurement data, the water surface measurement data, information on the height of the feature, and highest point information on the height from the reference position of the vessel to the highest point of the vessel; An information processing device having the above.

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