JP2023135106A - Information processor, control method, program and storage medium - Google Patents

Abstract

To provide an information processor which can suitably calculate a normal line of a measured point measured by a measurement device.SOLUTION: A controller 13 of an information processor 1 mainly includes acquisition means, normal line calculation range setting means, and normal line calculation means. The acquisition means acquires measurement data which is a set of data indicating a plurality of measured points measured by a measurement device. The normal line calculation range setting means sets a normal line calculation range for generating a point set of the measurement points used in calculation of the respective normal lines of the measured points, on the basis of a measurement distance indicated by the measurement data. The normal line calculation means calculates the respective normal lines of the measured points, on the basis of the normal line calculation range.SELECTED DRAWING: Figure 18

Description

The present disclosure relates to processing of measurement data.

BACKGROUND ART Conventionally, techniques for providing support for berthing (berthing) of ships have been known. For example, Patent Document 1 discloses, in an automatic berthing device that automatically berths a ship, control that changes the attitude of the ship so that light emitted from a lidar is reflected by objects around the berthing position and received by the lidar. The method to do this is described.

JP2020-59403A

Generally, a quay consists of a flat ground portion (top surface) and a vertical wall portion (side surface) that separates the ground from the sea. At this time, in order to extract the surface, it is desirable to use normal information calculated from the measured point cloud data as the basis. On the other hand, for example, in the case of lidar, the point cloud measured from a long distance is sparse, and the point cloud measured from a short distance is dense, so depending on the distance of the measured point, the normal calculation may be difficult. There was a possibility that the accuracy would deteriorate or calculation would become impossible.

The present disclosure has been made to solve the above-mentioned problems, and its main purpose is to provide an information processing device that can suitably calculate the normal line of a measured point measured by a measuring device. .

The claimed invention is:
acquisition means for acquiring measurement data that is a collection of data representing a plurality of measured points measured by the measurement device;
normal line calculation range setting means for setting a normal line calculation range for generating a point set of the measured points to be used for calculating the normal line of each of the measured points, based on the measurement distance indicated by the measurement data;
normal line calculation means for calculating the normal line of each of the measured points based on the normal line calculation range;
This is an information processing device having:

In addition, the claimed invention is
A control method executed by a computer,
Obtain measurement data, which is a collection of data representing multiple measured points measured by a measuring device,
Based on the measurement distance indicated by the measurement data, setting a normal calculation range for generating a point set of the measurement points used for calculating the normal of each of the measurement points,
calculating a normal to each of the measured points based on the normal calculation range;
This is a control method.

In addition, the claimed invention is
Obtain measurement data, which is a collection of data representing multiple measured points measured by a measuring device,
Based on the measurement distance indicated by the measurement data, setting a normal calculation range for generating a point set of the measurement points used for calculating the normal of each of the measurement points,
This is a program that causes a computer to execute a process of calculating a normal line of each of the points to be measured based on the normal line calculation range.

1 is a schematic configuration diagram of a navigation support system. FIG. 2 is a block diagram showing the hardware configuration of an information processing device. It is an example of the flowchart showing the process outline in an Example. It is a figure showing an outline of calculation processing of a berthing parameter. (A) An example of a ship coordinate system based on the hull of the target ship is shown. (B) It is a perspective view of a structure clearly showing a normal vector. (A) It is a front view of a target ship and a quay when a quay serving as a detection target exists relatively far away. (B) It is a front view of a target ship and a quay when a quay serving as a detection target exists relatively nearby. (A) An arrangement of measured points within a 1 m square showing the relationship between the distance between points at a 30 m point and the calculated normal radius. (B) represents a linear equation according to the measured distance x of the point of interest when the normal calculation radius is “y”. (A) A diagram showing normal lines to each measured point on the side surface of a quay wall with high flatness using arrows. (B) It is a figure showing the normal line to each measured point of the quay wall side surface with an unevenness|corrugation by the arrow. (A) to (C) are diagrams clearly showing the side points of the quay where fenders are installed. FIG. 3 is a diagram illustrating an overview of inter-plane distances used in same-plane integration processing. FIG. 3 is a diagram illustrating an overview of area non-wall surface removal processing. (A) An overview of the first pseudo area calculation method is shown. (B) An overview of the second pseudo area calculation method is shown. (A) A perspective view of a quay when there are three wall candidate clusters on the side and three wall candidate clusters on the top. (B) A perspective view of the quay clearly showing wall surface candidates corresponding to the top surface and the side surface. FIG. 3 is a cross-sectional view of the quay clearly showing the upper surface existing range. (A) and (B) are diagrams showing procedures (b) to (d) for selecting wall surface candidate clusters for the top and side surfaces, respectively. (A) It is a figure which shows the classification result of the measured point on a quay. (B) It is a figure which shows the classification result of the measured point after a boundary point determination process. (A) and (B) are diagrams showing an overview of boundary point determination processing. This is an example of a flowchart of normal line calculation processing. This is an example of a flowchart of wall orientation determination processing. It is an example of a flowchart of distributed non-wall surface removal processing and same surface integration processing. It is an example of the flowchart of area non-wall surface removal processing. It is an example of a flowchart of optimal wall surface selection processing. It is an example of the flowchart of a boundary point determination process. FIG. 3 is a diagram showing an overview of a first application example. FIG. 3 is a diagram showing an overview of a first application example. FIG. 7 is a diagram showing an outline of a second application example. FIG. 7 is a diagram showing an outline of a second application example. FIG. 7 is a diagram showing an outline of a third application example. FIG. 7 is a diagram showing an outline of a third application example. It is a table showing an example of calculation of quay detection reliability.

According to a preferred embodiment of the present disclosure, an information processing device includes an acquisition unit that acquires measurement data that is a collection of data representing a plurality of measured points measured by a measurement device, and a measurement distance indicated by the measurement data. a normal calculation range setting means for setting a normal calculation range for generating a point set of the measurement points used for calculating the normal of each of the measurement points, based on the normal calculation range; , normal line calculating means for calculating the normal line of each of the measured points. According to this aspect, the information processing device can suitably calculate the normal line of each measured point measured by the measuring device.

In one aspect of the information processing device, the normal line calculation range setting unit increases the normal line calculation range as the measured distance is longer. With this aspect, the information processing device can suitably calculate the normal line of each of the measurement points measured by the measurement device.

In another aspect of the information processing device, the information processing device includes a clustering unit for clustering the measured points based on the normal line, and a clustering unit that clusters the measured points based on the normal line, and each of the clusters of the measured points formed by the clustering. and determining means for determining whether or not the cluster is a candidate based on the degree of variation in the normal line within the cluster. According to this aspect, the information processing device can suitably detect a measurement point on the wall surface of the quay that has high flatness as a candidate for the quay.

In another aspect of the information processing device, the information processing device includes a clustering unit that performs clustering of the measured points based on the normal line, and a clustering unit that clusters the measured points based on the normal line; and determining means for estimating surfaces and determining whether or not the clusters need to be integrated based on the distance between the surfaces. According to this aspect, the information processing apparatus can suitably determine whether or not it is necessary to integrate clusters separated by obstacles or the like.

In another aspect of the information processing device, the information processing device includes a clustering unit for clustering the measured points based on the normal line, and a clustering unit that clusters the measured points based on the normal line, and each of the clusters of the measured points formed by the clustering. and determining means for determining whether or not the object is a candidate based on the number of points to be measured forming the cluster and the area of the cluster. This aspect allows the information processing device to accurately determine clusters that are candidates for quays.

In another aspect of the information processing device, the information processing device includes a clustering unit that clusters the measured points based on the normal line, and a clustering unit that clusters the measured points based on the normal, and the measured points that are candidates for the top surface of the quay formed by the clustering. A determining means is provided for determining, among the clusters of points, a cluster that exists within a predetermined existence range and that exists at the lowest position as a cluster representing the upper surface. This aspect allows the information processing device to accurately determine the cluster of measured points representing the top surface of the quay.

In another aspect of the information processing device, the determination means is located at a position lower than the cluster representing the top surface of the cluster of the measured points that are candidates for the side surface of the quay formed by the clustering. Then, the cluster closest to the measuring device is determined to be the cluster representing the side surface. This aspect allows the information processing device to accurately determine clusters of measured points representing the side surfaces of the quay.

In another aspect of the information processing device, the information processing device determines whether each of the measured points is a wall point where a quay is measured or a non-wall point where an area other than the quay is measured, based on the normal line. The classification means classifies the non-wall point existing between the wall points as the wall point based on the shortest distance between the non-wall point and the wall point. Classify. With this aspect, the information processing device can accurately classify the measured points near the boundary between the top surface and the side surface of the quay, which is difficult to classify accurately based only on the direction of the normal line.

According to another preferred embodiment of the present disclosure, there is provided a control method executed by a computer, in which measurement data, which is a set of data representing a plurality of measured points measured by a measuring device, is acquired, and the measurement data Based on the measurement distance indicated by Calculate the normal line of each measurement point. By executing this control method, the computer can suitably calculate the normal line of each point to be measured measured by the measuring device.

According to another preferred embodiment of the present disclosure, the program acquires measurement data that is a collection of data representing a plurality of measured points measured by a measuring device, and based on the measurement distance indicated by the measurement data, A normal calculation range for generating a point set of the measurement points used for calculating the normal of each of the measurement points is set, and the normal of each of the measurement points is calculated based on the normal calculation range. This is a program that causes a computer to execute the process of calculating . By executing this program, the computer can suitably calculate the normal line of each point to be measured measured by the measuring device. Preferably, the program is stored on a storage medium.

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

(1) Overview of the flight support system
FIG. 1(A) to FIG. 1(C) are schematic configurations of the navigation support system according to this embodiment. Specifically, FIG. 1(A) shows a block configuration diagram of the navigation support system, and FIG. 1(B) shows the visual field range (“measurement range” or FIG. 1C is a top view illustrating the visual field range 90 of the ship and the rider 3 from behind. The navigation support system includes an information processing device 1 that moves together with a ship that is a mobile object, and a sensor group 2 that is mounted on the ship. Hereinafter, a ship equipped with a navigation support system will also be referred to as a "target ship."

The information processing device 1 is electrically connected to the sensor group 2 and supports the operation of the target ship based on the outputs of various sensors included in the sensor group 2. Operation support includes berthing support such as automatic berthing (berthing). In this embodiment, accurate navigation support is achieved by accurately measuring the quay that is the berthing location. The information processing device 1 may be a navigation device provided on a ship, or may be an electronic control device built into the ship.

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.

The lidar 3 exists in the outside world by emitting a pulsed laser in a predetermined horizontal angular range (see FIG. 1(B)) and a predetermined vertical angular range (see FIG. 1(C)). It is an external sensor that discretely measures the distance to an object and generates three-dimensional point cloud data indicating the position of the object. In the examples shown in FIGS. 1(B) and 1(C), a rider 3 directed toward the left side of the ship and a rider directed toward the right side of the ship are provided on the ship, respectively. . Note that the arrangement of the rider 3 is not limited to the examples shown in FIGS. 1(B) and 1(C). For example, the target ship has multiple lidars 3 (for example, lidars installed in front and rear of the target ship) that measure the same lateral direction so that multiple measurement data of the quay can be obtained simultaneously when berthing. It's okay. Further, the number of riders 3 installed on the target ship is not limited to two, but may be one, or three or more.

The lidar 3 includes an irradiation section that irradiates laser light while changing the irradiation direction, a light reception section that receives reflected light (scattered light) of the irradiated laser light, and outputs scan data based on the light reception signal output by the light reception section. It has an output section. The data measured for each laser beam irradiation direction (scanning position) is based on the irradiation direction corresponding to the laser beam received by the light receiving unit and the response delay time of the laser beam specified based on the above-mentioned light reception signal. Generated based on Hereinafter, a point measured by laser beam irradiation within the measurement range of the lidar 3 or its measurement data will also be referred to as a "measured point."

Here, the point cloud data can be regarded as an image (frame) in which each measurement direction is a pixel, and the measurement distance and reflection intensity value in each measurement direction are pixel values. In this case, the emitting direction of the laser beam at the elevation/depression angle (that is, the measurement direction) differs in the vertical arrangement of the pixels, and the emitting direction of the laser light at the horizontal angle differs in the horizontal arrangement of the pixels. Hereinafter, when the point cloud data is regarded as an image, the measured points corresponding to columns (that is, vertical columns) of pixels whose index positions in the horizontal direction match are also referred to as "vertical lines."

Note that the lidar 3 is not limited to the above-mentioned scan type lidar, but may be a flash type lidar that generates three-dimensional data by diffusing laser light into the field of view of a two-dimensional array of sensors. The rider 3 is an example of a "measuring device" in the present invention.

(2) Configuration of information processing device
FIG. 2 is a block diagram showing an example of the hardware configuration of the information processing device 1. As shown in FIG. The information processing device 1 mainly includes an interface 11, a memory 12, and a controller 13. Each of these elements is interconnected via a bus line.

The interface 11 performs interface operations related to data exchange between the information processing device 1 and external devices. In this embodiment, the interface 11 acquires output data from each sensor of the sensor group 2 and supplies it to the controller 13. Further, the interface 11 supplies, for example, a signal related to the control of the target ship generated by the controller 13 to each component of the target ship that controls the operation of the target ship. For example, the target ship has a drive source such as an engine or an electric motor, a screw that generates propulsive force in the forward direction based on the driving force of the drive source, and a thruster that generates lateral propulsive force based on the driving force of the drive source. and a rudder, etc., which is a mechanism for freely determining the direction of travel of the vessel. During automatic operation such as automatic berthing, the interface 11 supplies control signals generated by the controller 13 to each of these components. Note that if the target ship is provided 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 or the like. Further, the interface 11 may perform interface operations with various peripheral devices such as an input device, a display device, and a sound output device.

The memory 12 includes various types of volatile memory and nonvolatile memory, such as RAM (Random Access Memory), ROM (Read Only Memory), hard disk drive, and flash memory. The memory 12 stores programs for the controller 13 to execute predetermined processes. Note that the program executed by the controller 13 may be stored in a storage medium other than the memory 12.

Further, the memory 12 stores information necessary for processing executed by the information processing device 1 in this embodiment. For example, the memory 12 may store map data including information regarding the location of the quay. In another example, the memory 12 stores information regarding the size of downsampling when downsampling is performed on point cloud data obtained when the lidar 3 scans for one cycle.

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 a program stored in the memory 12 or the like to perform processing related to operational support for the target vessel.

Further, the controller 13 functionally includes a quay detection section 15 and a berthing parameter calculation section 16. The quay detection unit 15 performs processing related to quay detection based on the point cloud data output by the rider 3. The berthing parameter calculation unit 16 calculates parameters necessary for berthing to a quay (also referred to as "berthing parameters"). Here, the berthing parameters include the distance from the target vessel to the quay (the opposite shore distance), the approach angle of the target vessel to the quay, the speed at which the target vessel approaches the quay (berthing speed), and the like. Furthermore, the berthing parameter calculation unit 16 may calculate information representing reliability regarding berthing to the berthing (also referred to as “reliability information”) based on the processing results of the berth detection unit 15 and the berthing parameters. The controller 13 functions as an "acquisition means", "normal line calculation range setting means", "normal line calculation means", "clustering means", "judgment means", "classification means", and a computer that executes a program. do.

Note that the processing executed by the controller 13 is not limited to being implemented by software based on a program, but may be implemented by a combination of hardware, firmware, and software. Further, the processing executed by the controller 13 may be realized using a user-programmable integrated circuit such as an FPGA (Field-Programmable Gate Array) or a microcomputer. In this case, this integrated circuit may be used to realize the program that the controller 13 executes in this embodiment.

(2) Processing overview
FIG. 3 is an example of a flowchart showing an overview of the processing executed by the quay detection unit 15 in this embodiment.

First, the quay detection unit 15 acquires point cloud data generated by the rider 3 every frame period (step S1). Note that the quay detection unit 15 may remove data existing below the water surface position from the point cloud data generated by the rider 3 as water surface measurement data (ie, erroneous detection data). Note that the quay detection unit 15 estimates the water surface position based on, for example, the average value in the height direction of point cloud data generated by the lidar 3 when there are no objects other than the water surface in the vicinity. Furthermore, the quay detection unit 15 may perform downsampling, which is a process of integrating measured points for each grid space of a predetermined size, on the point cloud data after removing the water surface reflection data. Note that downsampling may be performed before removing false detection data.

Next, the quay detection unit 15 executes a normal calculation process, which is a process of calculating the normal to each measured point represented by the point cloud data (step S2). Then, the quay detection unit 15 performs a wall orientation determination process based on the orientation of the normal line calculated in step S2 (step S3). In the wall orientation determination process, the quay detection unit 15 determines, for each measured point, a measured point on the top surface of the quay (also referred to as "top point") and a measured point on the side surface of the quay (also referred to as "side point"). ), or a measured point that is not a quay (also called a "non-wall point").

Next, the quay detection unit 15 performs a dispersion correction process, which is a process of removing non-wall points that are incorrectly labeled as top points or side points (also referred to as "wall points"), based on the dispersion of the normals. Wall surface removal processing is performed (step S4). Further, the quay wall detection unit 15 performs a coplanar integration process, which is a process of integrating clusters of wall surface points, in order to treat a wall surface divided by fenders or the like as one surface (step S5). Then, the quay detection unit 15 performs area non-wall surface removal processing, which is processing for removing noise points generated by noise and measurement points of small objects based on the number of cluster points and area (step S6).

Next, the quay detection unit 15 executes optimal wall surface selection processing, which is processing for selecting a set of clusters of top surface points and clusters of side points when there are multiple clusters of top surface points and multiple clusters of side points. (Step S7). Next, the quay detection unit 15 determines whether a non-wall point (also referred to as a "boundary point") that exists between the top surface point and the side point is a wall surface point that exists on the boundary of the wall surface (the boundary between the top surface and the side surface). Boundary point determination processing is performed to determine whether or not (step S8). Then, the quay detection unit 15 outputs a quay point group that is point group data representing the measured points determined to be wall points corresponding to either top points or side points (step S9). Thereafter, the berthing parameter calculation unit 16 calculates berthing parameters (berthing distance, berthing speed, approach angle) based on the quay point group.

FIG. 4 is a diagram showing an overview of the berthing parameter calculation process by the berthing parameter calculation unit 16. The berthing parameter calculation unit 16 generates a straight line (also referred to as a "quay edge straight line") that is closest to the target ship for a group of quay points (specifically, side points). Then, the berthing parameter calculation unit 16 calculates the shortest distance to the straight line as the distance to the opposite shore. The berthing parameter calculation unit 16 also calculates the berthing speed by time differentiation of the opposite shore distance, and calculates the approach angle representing the bow direction from the angle with respect to the quay edge straight line. Here, if non-wall points such as small objects that are not quay walls and noise cannot be removed from the point group of measured points measured by the rider 3, the berthing parameters cannot be calculated accurately. In consideration of the above, in this embodiment, a method for accurately extracting a group of quay points by the quay detection unit 15 will be described.

(3) Normal calculation process
In the normal calculation process, the quay detection unit 15 calculates a normal vector to each measured point represented by the point cloud data acquired by the rider 3. In this case, the quay detection unit 15 sequentially selects each measured point as a point to be processed (also referred to as a "point of interest"), and selects each measured point as a point to be processed (also referred to as a "point of interest"), and selects a target point that is approximated by a group of measured points existing within a certain distance from the point of interest. Calculate the normal vector of the surface. Then, the quay detection unit 15 determines whether each measured point is a side point or Classify (label) whether it corresponds to a top surface point or a non-wall surface point.

FIG. 5(A) shows an example of a ship coordinate system based on the hull of the target ship. As shown in Figure 5 (A), here, the front (forward) direction of the target ship is the "X" coordinate, the side direction of the target ship is the "Y" coordinate, and the height direction of the target ship is the "Z" coordinate. do. Then, the measurement data measured by the rider 3 in the coordinate system based on the rider 3 is converted into the ship coordinate system shown in FIG. 5(A). Note that processing for converting point cloud data in a coordinate system based on a lidar installed on a moving body into a coordinate system of the moving body is disclosed in, for example, International Publication WO 2019/188745.

FIG. 5(B) is a perspective view of the quay clearly showing the measured point representing the measured position measured by the rider 3 and the normal vector calculated based on the measured point. In FIG. 5(B), the measured points are indicated by circles, and the normal vectors are indicated by arrows. Here, an example is shown in which both the top and side surfaces of the quay were able to be measured by the rider 3.

As shown in FIG. 5(B), the quay detection unit 15 calculates normal vectors to the measured points on the side and top surfaces of the quay. Since the normal vector is a vector perpendicular to the target plane or curved surface, it is calculated using a plurality of measured points that can be configured as a surface. Therefore, the quay detection unit 15 sets a circle with a predetermined radius for the selected point of interest, and calculates a normal vector using the measured points existing inside the circle. Hereinafter, the above-mentioned radius will be referred to as a "normal calculation radius", and the above-mentioned circle (or sphere) will also be referred to as a "normal calculation range".

Here, the quay detection unit 15 determines the normal calculation radius according to the measured distance of the point of interest. This specific example will be explained with reference to FIGS. 6(A) and 6(B). FIG. 6(A) is a front view of the target ship and the quay when the quay to be detected is relatively far away, and FIG. 6(B) is a front view of the quay to be detected when the quay is relatively close. FIG. In Figures 6(A) and 6(B), the measured points on the quay are indicated by circles, and furthermore, the normal calculation range for a certain measured point is indicated by a solid line frame, and the normal is indicated by an arrow. It is shown.

As shown in FIG. 6A, when the quay is located relatively far away, the quay detection unit 15 detects that the number of point cloud data is small (that is, the measured points are sparsely distributed on the quay) Considering that normal calculation is difficult, set a large normal calculation radius. Thereby, the normal calculation range can be enlarged, and the number of measured points used for normal calculation can be suitably secured.

On the other hand, the quay detection unit 15 sets the normal calculation radius small when the quay is present at a short distance as shown in FIG. 6(B). This is because if the same normal calculation radius is used for short distances as for long distances, the number of measured points used for normal calculation will become too large, increasing calculation costs and reducing orientation estimation accuracy. This is because such problems arise.

FIG. 7(A) shows an arrangement of measured points within a 1 m square, showing the relationship between the distance between points at a 30 m point and the calculated normal radius. Here, "φ" indicates the resolution of the lidar 3, and "x" indicates the measured distance of the point of interest (the center measured point in FIG. 7(A)). In this case, the distance between adjacent measurement points is represented by "φx". Further, in this embodiment, the normal calculation radius is set for each point of interest, and is expressed by a linear equation that increases in proportion to the measured distance of the point of interest. In this case, if "a" is the slope of the linear equation (inter-point distance magnification parameter) and "b" is the intercept of the linear equation, the normal calculation radius is expressed by "aφx+b" for the inter-point distance. FIG. 7(B) shows a linear equation according to the measured distance x of the point of interest when the normal calculation radius is "y". Here, the coefficients a and b are set in advance based on experiments or the like so that at least five points are included in the normal calculation range, and are stored in the memory 12 or the like.

In this way, the quay detection unit 15 adaptively sets the normal calculation radius according to the measured distance of the point of interest, so that the normal for each measured point can be stably determined by a fixed number of points regardless of the distance. It becomes possible to calculate

Thereafter, in the wall orientation determination process, the quay detection unit 15 determines whether each measured point is a top surface point, a side surface point, or a non-wall surface point based on the normal angle, which is the angle of the normal to the XY plane in the ship coordinate system. Perform the classification. In this case, the quay detection unit 15 classifies the measured point whose normal angle is equal to or greater than a predetermined upper surface threshold as an upper surface point, and the quay wall detection unit 15 classifies the measured point whose normal angle is less than a predetermined side threshold (a threshold lower than the upper surface threshold). The measured points where , are classified as side points, and the other measured points are classified as non-wall points.

(4) Distributed non-wall surface removal processing
Since the target quay does not necessarily have sides perpendicular to the ground, it is necessary to have a certain width in the range of normal angles that are determined to be wall surfaces in order to accommodate various quays. be. On the other hand, if the range of normal angles that are determined to be wall surfaces is increased, there is a risk that surfaces that are not flat may also be determined to be wall surfaces. In this way, it is necessary to accurately determine the measurement point on the quay surface, excluding areas with severe unevenness due to fenders, etc., as the wall surface point.

Taking the above into consideration, in the distributed non-wall surface removal process, the quay wall detection unit 15 forms clusters with large normal variances as non-wall points in the clusters of top surface points and side points determined in the wall orientation determination process. Remove as a cluster. Note that the method (clustering method) for generating clusters of top surface points and side points is any clustering method such as Euclidean clustering based on distance, for example.

FIG. 8(A) is a diagram in which arrows indicate normal lines to each measurement point on the side surface of the quay wall, which has high flatness. FIG. 8(B) is a diagram in which arrows indicate normal lines to each point to be measured on the side surface of the quay wall with projections and depressions. Here, "θ" represents a normal angle to a certain measured point. In the example of FIG. 8(A), the directions of the normals to each measured point on the side surface of the quay are the same, and the dispersion is small. Therefore, in this case, the quay wall detection unit 15 determines each measured point on the side surface of the quay to be a wall surface point. In Fig. 8(A), the side surface is obliquely inclined, but by increasing the permissible angle of the normal angle θ (i.e., the above-mentioned side surface threshold) to a certain extent, it is possible to measure points on such a side surface. It is possible to accurately determine that the point is a wall point.

On the other hand, in the example shown in FIG. 8(B), there is variation in the direction of the normal to each measured point on the side surface of the quay, and the variance is large. Therefore, in this case, the quay detecting unit 15 determines each measured point on the side of the quay to be a non-wall surface point.

Here, a specific example of a method for calculating the variance for each cluster will be described. First, the quay detection unit 15 calculates the covariance matrix of each element in the XYZ directions of the group of normals to the measured point for each cluster. Since the diagonal components of the covariance matrix represent the variance values in the directions of each axis (X axis, Y axis, Z axis), the quay detection unit 15 determines whether the maximum value of the diagonal components of the covariance matrix is greater than or equal to a predetermined threshold. If so, it is determined that the target cluster is a cluster of non-wall points.

According to the above-mentioned distributed non-wall surface removal process, even if the range of normal angles that are determined to be wall surfaces is increased to a certain extent, the measured surface of a surface with low flatness (a surface with varying normals and large dispersion) Points can be accurately determined as non-wall points. In addition, since the range of normal angles that are determined to be wall surfaces can be increased to a certain extent, it is possible to accurately determine measured points on oblique side surfaces as wall points as shown in FIG. It becomes possible to determine wall points robustly even when the angle changes. Note that the variance is an example of an index representing the "degree of dispersion."

(5) Same-plane integration processing
When detecting a quay, if spatially close points are divided into clusters using Euclidean clustering, etc., clusters of wall candidates that should originally exist on the same plane may be created due to fenders or obstacles on the quay. However, it may be divided into multiple clusters. If only one of the clusters of wall candidates is adopted as a cluster representing a wall surface, the number of wall points used for calculating the opposite shore distance by the berthing parameter calculation unit 16 will decrease, and there is a risk that the accuracy of calculating the opposite shore distance will deteriorate. .

Taking the above into consideration, in the same surface integration process, clusters of side points or top surface points (also referred to as "wall surface candidate clusters") are integrated based on the direction of the normal line and the inter-plane distance between wall surface candidate clusters. A) is a diagram that clearly shows the side points of the quay where fenders are installed. In FIG. A wall surface candidate cluster and a second wall surface candidate cluster (here both clusters of side points) are formed. In FIG. 9B, the second wall surface candidate cluster is adopted as a cluster representing a wall surface, and the first wall surface candidate cluster is This represents a state where the wall candidate cluster is a cluster representing a non-wall surface.In this example, the measured points forming the first wall candidate cluster are classified as non-wall points, so the number of side points is reduced as a result. FIG. 9(C) is a diagram showing an integrated wall candidate cluster obtained by integrating the first wall candidate cluster and the second wall candidate cluster by the same surface integration process. In this example, the first wall candidate cluster and Since the measured points belonging to the second wall candidate cluster are treated as side points, it is possible to suppress the decrease in the number of side points.In addition, the generated integrated wall candidate cluster is used in the area non-wall surface removal process described later. It also has the advantage that it is less likely to be excluded as a non-wall surface.

FIG. 10 is a diagram illustrating an overview of inter-plane distances used in the same-plane integration process. In FIG. 10, "G ₁ (x ₁ , y ₁ , z ₁ )" represents the representative point (representative position) of the first wall candidate cluster, and "G ₂ (x ₂ , y ₂ , z ₂ )" represents the representative point (representative position) of the first wall candidate cluster. , represents the representative position of the second wall candidate cluster. Furthermore, “n=(n _x , _ny , _nz ) ^T ” is the normal vector of the representative of the first wall candidate cluster (same as the normal vector of the representative of the second wall candidate cluster). . Moreover, "H" represents the intersection of the surface (extension surface) that approximates the second wall surface candidate cluster and the normal vector n.

As shown in FIG. 10, the quay wall detection unit 15 calculates the inter-plane distance by calculating the inner product (|(G2-G1 )・n|). Since this inter-plane distance can be calculated without changing even if the roll/pitch angle of the target ship changes, the same-plane integration process can be accurately executed regardless of the attitude of the target ship.

Next, the integration conditions will be explained. The first condition of the quay detection unit 15 is that the normals of the wall candidate clusters to be compared are approximate. For example, in the case of side candidate clusters, if the angular difference on the XY plane is within 10 degrees, it is determined that the normal lines are similar. Next, the quay detection unit 15 sets a second condition that the inter-plane distance is less than a predetermined threshold. Furthermore, the quay wall detection unit 15 sets a third condition that the distance between the representative points of the wall surface candidate clusters to be compared is less than a predetermined threshold value. Note that the third condition does not have to be an essential condition. In this case, the first condition and the second condition may be necessary and sufficient conditions, and the first to third conditions may be necessary and sufficient conditions.

Here, a supplementary explanation will be given regarding the integration method when there are three or more wall candidate clusters. In the first example, the quay wall detection unit 15 integrates the set of wall candidate clusters that meet the above conditions, recalculates the representative normal and representative point, and then compares and integrates the clusters with the next candidate. Perform sequentially. In the second example, the quay detection unit 15 performs an integration determination between wall candidate clusters that are close to each other, and finally integrates all wall candidate clusters that have been determined to be integrated.

(6) Area non-wall surface removal processing
In area non-wall surface removal processing, the quay wall detection unit 15 removes wall surface candidate clusters corresponding to noise points and small objects (such as mooring ropes) based on the number of points and the area.

First, the quay detection unit 15 excludes outlying points such as noise by treating a small wall candidate cluster whose number of points is less than a predetermined threshold as a cluster of non-wall points. Next, the quay detection unit 15 adds the area occupied by the wall candidate cluster to the determination criteria, and excludes small wall candidate clusters that do not satisfy the predetermined area as clusters of non-wall points.

FIG. 11 is a diagram illustrating an overview of area non-wall surface removal processing. In FIG. 11, each of the measured points P1 to P3 is a cluster consisting of one point, and since the number of points of these clusters is less than the threshold value, the quay wall detection unit 15 regards these clusters as non-wall points. Furthermore, although the number of points in the cluster Cw4 is equal to or greater than the threshold, it does not satisfy the area threshold, so the quay detection unit 15 considers the cluster Cw4 to be a cluster of non-wall points. On the other hand, the quay detection unit 15 leaves clusters Cw5 and Cw6 that satisfy both the score and area conditions as wall surface candidate clusters.

Here, a method for calculating the area of wall candidate clusters will be explained. The quay detection unit 15 performs principal component analysis for each wall candidate cluster and calculates the pseudo area occupied by each cluster. FIG. 12(A) shows an overview of the first pseudo area calculation method, and FIG. 12(B) shows an overview of the second pseudo area calculation method. In FIG. 12(A), "PC ₁ " is the first principal component axis of cluster Cw5, "PC ₂ " is the second principal component axis of cluster Cw5, "PC ₃ " is the first principal component axis of cluster Cw6, " PC ₄ ” represents the second principal component axis of cluster Cw6. In addition, in FIG. 12(B), "sd ₁ " is the standard deviation of the first principal component axis of cluster Cw5, "sd ₂ " is the standard deviation of the second principal component axis of cluster Cw5, and "sd ₃ " is the standard deviation of the second principal component axis of cluster Cw6. The standard deviation of the first principal component axis of "sd ₄ " represents the standard deviation of the second principal component axis of cluster Cw6.

In the first pseudo area calculation method, the quay detection unit 15 assumes that the lengths in the first and second principal component axes of the principal component analysis are the vertical and horizontal lengths, and that the cluster Cw5 and the cluster Cw6 are respectively considered as quadrilaterals. Calculate the area of . In this case, using the coefficient k (0<k<1), the quay detection unit 15 calculates the area _S1 of the cluster Cw5 based on the first pseudo area calculation method.
S ₁ =k× _ws × _hs
And the area S1 of cluster Cw6 is
S ₁ =k x w _u x h _u
shall be. In this case, the lengths (ws _, hs _{, wu, hu} ) are determined by the difference between the maximum and minimum values of the measured points in the cluster in the first and second principal component axis directions. In the first pseudo area calculation method, when the cluster is close to a quadrangle, a value close to the actual area is obtained.

In the second pseudo area calculation method, the quay detection unit 15 calculates the standard deviation from the eigenvalue of the first principal component and the eigenvalue of the second principal component, sets the product as the cluster size (that is, the degree of dispersion), and calculates the area. calculate. In this case, the quay detection unit 15 calculates the area _S2 of the cluster Cw5 based on the second pseudo area calculation method.
S ₂ =k×sd ₁ ×sd ₂ =k√(λ ₁ λ ₂ )
and the area S2 of cluster Cw6 is
S ₂ =k×sd ₃ ×sd ₄ =k√(λ ₃ λ ₄ )
shall be.

Here, "λ ₁ " is the eigenvalue of the first principal component of cluster Cw5, "λ ₂ " is the eigenvalue of the second principal component of cluster Cw5, "λ ₃ " is the eigenvalue of the first principal component of cluster Cw6, "λ ₄ '' represents the eigenvalue of the second principal component of cluster Cw6. With the second pseudo area calculation method, the sizes of various shapes can be evaluated in a general-purpose manner.

(7) Optimal wall surface selection process
If the target quay has a complex shape, there are multiple wall surface candidate clusters. Therefore, the quay detection unit 15 selects wall surface candidate clusters that are likely to be quays in the optimal wall surface selection process.

In this case, the quay detection unit 15 calculates the representative position for each wall surface candidate cluster, and selects the optimal surface based on the mutual positional relationship. Finally, the quay detection unit 15 narrows down the wall surface candidate clusters corresponding to the top surface and the side surface to one each, and regards the unselected wall surface candidate clusters as clusters of non-wall surface points such as obstacles. FIG. 13(A) shows a perspective view of a quay when there are three side wall candidate clusters and three top wall candidate clusters. FIG. 13(B) shows a perspective view of the quay clearly showing wall surface candidates corresponding to the top surface and side surface.

The details of the optimal wall surface selection process will be explained. First, the quay detection unit 15 presets a range of heights in which the top surface of the quay can exist (also referred to as "top surface existence range"), and sets wall surface candidate clusters on the top surface that are outside the range (for example, sea surface reflection points and construction Clusters such as roofs of objects) are excluded.

FIG. 14 is a sectional view of the quay clearly showing the upper surface existing range. In FIG. 14, clusters Cw10 to Cw14 are wall surface candidate clusters consisting of top surface points. In this case, the quay detection unit 15 regards the clusters Cw10 and Cw13, which are outside the upper surface existence range, as clusters of non-wall points. Note that the upper surface existence range changes depending on the tide level and the draft depth of the ship, so it should be set within a range with some leeway. The quay detection unit 15 also sets an existence range (side existence range) for side surfaces when the existence range is limited, and classifies wall candidate clusters of side points outside the side existence range as clusters of non-wall points. You may.

15(A) and 15(B) are diagrams showing procedures (b) to (d) for selecting wall surface candidate clusters for the top surface and side surface, respectively. Note that step (a) indicates that surfaces are integrated by the same surface integration process as preprocessing. First, in step (b), the quay detection unit 15 selects a wall candidate cluster whose representative position is the lowest among the upper wall candidate clusters existing in the upper surface existence range. Here, the representative position is, for example, the center of gravity or the geometric center of the wall candidate cluster. Next, in step (c), the quay wall detection unit 15 excludes the wall surface candidate cluster on the side surface whose representative position is higher than the representative position of the wall surface candidate cluster corresponding to the top surface selected in step (b). Then, the quay detection unit 15 selects the wall candidate cluster that is the representative position closest to the target ship from among the remaining side wall candidate clusters.

According to these procedures, the quay wall detection unit 15 can accurately select a set of wall surface candidate clusters corresponding to the top surface and the side surface, respectively.

(8) Boundary point determination processing
Near the edge of the quay, when calculating the normal, points on the top and side surfaces are mixed, so the normal angle tends to be diagonal. Therefore, for measured points near edges, it is difficult to accurately determine whether they are top points, side points, or non-wall points based on the normal angle. FIG. 16(A) is a diagram showing the classification results of measurement points on the quay. In FIG. 16(A), the arrow represents the normal direction. In the example of FIG. 16A, the measured point near the edge of the boundary between the side surface and the top surface is determined to be a non-wall surface point.

Taking the above into consideration, in the boundary point determination process, the quay detection unit 15 re-determines whether a boundary point that is a non-wall point existing at the boundary between the top surface and the side surface is suitable as a wall surface point. FIG. 16(B) is a diagram showing the classification results of the measured points after the boundary point determination process. In this case, the non-wall points that existed in FIG. 16(A) have been appropriately reclassified as either side points or top points.

FIG. 17(A) and FIG. 17(B) are diagrams showing an overview of the boundary point determination process. First, as shown in FIG. 17(A), the quay detection unit 15 searches for a boundary point by scanning each vertical line from below (that is, from the position where the angle of depression of the rider 3 is maximum to the direction where the angle of elevation increases). do. Next, as shown in FIG. 17(B), the quay detection unit 15 calculates the distance from the searched boundary point to the nearest wall point (side point or top point), and determines that the distance is a predetermined threshold value. If the following is true, the searched boundary point is re-determined (classified) as a wall point. Specifically, in this case, the quay detection unit 15 classifies the boundary point re-determined to be a wall point as a side point if the nearest wall point is a side point, and classifies the boundary point as a side point. If is a top point, it is classified as a top point. Note that even if only one side of the top or side surface is detected, the quay detection unit 15 regards the measured points in several rows from the end points of the wall points as boundary points, and the shortest distance between the boundary point and the wall point is the threshold. If it is below, the boundary point may be re-determined as a wall point.

(9) Detailed processing flow
Next, the detailed flow of each process explained in the flowchart of FIG. 3 will be explained.

FIG. 18 is an example of a flowchart of the normal calculation process executed in step S2 of FIG. 3.

First, the quay detection unit 15 acquires point cloud data used in the normal line calculation process (step S11). Then, the quay detection unit 15 executes a processing loop of steps S12 to S16 described below. In this case, the quay detection unit 15 sequentially selects all measured points of the acquired point cloud data as points of interest, and repeatedly executes a processing loop until there are no measured points to be selected as points of interest.

First, the quay detection unit 15 calculates the distance (i.e., measured distance) x from the reference point of the rider 3 to the point of interest (step S12). Next, the quay detection unit 15 calculates a normal radius based on the measured distance x (step S13). Then, the quay detection unit 15 searches for other measured points (also referred to as "neighboring points") that exist within the normal calculation radius from the point of interest (step S14). Then, the quay detection unit 15 determines whether the total score of the point of interest and the neighboring points is equal to or greater than the score threshold (step S15). If the above-mentioned score is equal to or greater than the score threshold (step S15; Yes), the quay detection unit 15 calculates the normal of the point of interest based on the point of interest and neighboring points (step S16). On the other hand, if the above-mentioned score is less than the score threshold (step S15; No), the quay detection unit 15 does not execute step S16.

Then, after completing the processing loop of steps S12 to S16, the quay detection unit 15 generates a group of points with normals in which a normal (normal vector) is associated with each measured point as a processing result of the normal calculation process. Output (step S17).

FIG. 19 is an example of a flowchart of the wall orientation determination process executed in step S3 of FIG.

First, the quay detection unit 15 obtains a group of points with normals, which are the processing results of the normal calculation process, as an input for use in the wall orientation determination process (step S21). Next, the quay detection unit 15 executes a processing loop of steps S22 to S26. In this case, the quay detection unit 15 sequentially selects all measured points of the acquired point cloud data as points of interest, and repeatedly executes a processing loop until there are no measured points to be selected as points of interest.

First, the quay detection unit 15 determines whether the normal angle θ of the point of interest is larger than the upper surface threshold (step S22). If the normal angle θ of the point of interest is larger than the top surface threshold (step S22; Yes), the quay detection unit 15 gives the point of interest a label (top surface label) indicating that it is a top surface point (step S22; Yes). S23). On the other hand, if the normal angle θ of the point of interest is less than or equal to the top threshold (step S22; No), the quay detection unit 15 determines whether the normal angle θ of the point of interest is less than the side threshold (step S24). . Then, if the normal angle θ of the point of interest is less than the side surface threshold (step S24; Yes), the quay detection unit 15 gives the point of interest a label (side label) indicating that it is a side point ( Step S25). On the other hand, if the normal angle θ of the point of interest is equal to or greater than the side surface threshold (step S24; No), the quay detection unit 15 gives the point of interest a label (non-wall label) indicating that it is a non-wall point. (Step S26). The above-mentioned labels (top label, side label, non-wall label) are collectively referred to as "wall label."

Then, after completing the processing loop of steps S22 to S26, the quay detection unit 15 generates a normal/wall labeled point group in which a normal and a wall label are associated with each measured point as a processing result of the wall orientation determination process. is output (step S27).

FIG. 20 is an example of a flowchart of the distributed non-wall surface removal process and the same surface integration process that are executed in step S4 and step S5 of FIG.

First, the quay detection unit 15 obtains a group of points with normal/wall labels, which are the processing results of the wall orientation determination process, as input for use in the distributed non-wall surface removal process and the same surface integration process (step S31). Then, the quay detection unit 15 performs clustering (for example, Euclidean clustering) of the measured points that become wall points (step S32). Then, the quay detection unit 15 determines whether or not the score of each of the generated clusters is greater than a predetermined threshold (step S33). Then, the quay detection unit 15 determines a cluster whose number of points is less than or equal to a predetermined threshold value as a cluster of non-wall points (step S33; No and step S34). In this case, the quay detection unit 15 changes the wall label of each measured point of the cluster whose score is below a predetermined threshold value to a non-wall label.

Next, the quay detection unit 15 separates the top surface point and the side surface point, and performs clustering (for example, Euclidean clustering) on each (step S35). The quay detection unit 15 then executes a processing loop of steps S36 to S45. In this case, the quay detection unit 15 sequentially selects the clusters generated in step S35 as clusters of interest, and repeatedly executes this processing loop until there are no more clusters to be selected as clusters of interest.

First, the quay detection unit 15 calculates a variance-covariance matrix for each measured point of the cluster of interest (step S36). The quay detection unit 15 then finds the maximum value of the diagonal components of the variance-covariance matrix (step S37). Next, the quay detection unit 15 determines whether the maximum value obtained in step S37 is less than a predetermined threshold (step S38). If the maximum value obtained in step S37 is less than the predetermined threshold (step S38; Yes), the quay detection unit 15 determines that the cluster of interest satisfies the conditions for a cluster of wall points, and executes step S39. . On the other hand, if the maximum value determined in step S37 is equal to or greater than the predetermined threshold (step S38; No), the quay detection unit 15 determines the cluster of interest to be a cluster of non-wall points (step S40).

In step S39, the quay detection unit 15 calculates the representative position (representative point) and representative normal of the cluster of interest (step S39). Then, based on the representative normal, the quay wall detection unit 15 determines whether there is another cluster whose representative normal is similar in direction to the cluster of interest (that is, toward the wall) (step S41). If there is another cluster having a similar wall orientation to the cluster of interest (Step S41; Yes), the quay detection unit 15 calculates the inter-plane distance between the cluster of interest and the other cluster (Step S42). Then, when the quay detection unit 15 determines that the inter-plane distance is less than the distance threshold (step S43; Yes), it integrates the point cloud of the cluster of interest and the other cluster (step S44), and The cluster representing the group is stored in the memory 12 or the like as a wall candidate cluster (step S45). On the other hand, when the quay detection unit 15 determines that there is no other cluster whose wall orientation is close to the cluster of interest (step S41; No), or that the distance between surfaces is equal to or greater than the distance threshold (step S43; No) , the cluster of interest is stored in the memory 12 or the like as a wall candidate cluster (step S45).

Then, after completing the processing loop related to clusters, the quay detection unit 15 outputs a group of wall surface candidate clusters as the processing results of the distributed non-wall surface removal process and the same surface integration process (step S46).

FIG. 21 is an example of a flowchart of the area non-wall surface removal process executed in step S6 of FIG.

First, the quay detection unit 15 obtains a group of wall candidate clusters, which are the processing results of the distributed non-wall surface removal process and the same surface integration process, as input to the area non-wall surface removal process (step S51). The quay detection unit 15 then executes a processing loop of steps S52 to S59. In this case, the quay detection unit 15 sequentially selects wall surface candidate clusters as clusters of interest, and repeatedly executes this processing loop until there are no wall surface candidate clusters to be selected as clusters of interest. Note that the quay detection unit 15 may execute steps S53 to S55 based on the first pseudo area calculation method, or may execute steps S57 and S58 based on the second pseudo area calculation method.

First, the quay detection unit 15 performs principal component analysis on the cluster of interest (step S52). Then, when adopting the first pseudo area calculation method, the quay detection unit 15 converts the original coordinate values of each measured point constituting the cluster of interest into coordinate values of the first and second principal component axes. (Step S53). Next, the quay detection unit 15 calculates the vertical and horizontal lengths w and h when the cluster of interest is regarded as a rectangle from the difference between the maximum value and the minimum value in each axis direction of the cluster of interest (step S54), and calculates the area _S1 is calculated (step S55). On the other hand, when adopting the second pseudo area calculation method, the quay detection unit 15 calculates the standard deviation sd from the eigenvalue λ of the first and second principal components (step S57), and calculates the area _S2 ( Step S58).

Then, the quay detection unit 15 determines whether the calculated area is larger than a predetermined threshold (step S56). Then, if the calculated area is less than or equal to the predetermined threshold (step S56; Yes), the quay wall detection unit 15 determines that the target cluster is a cluster of non-wall points (step S59).

After the wall candidate cluster processing loop ends, the quay detection unit 15 outputs the group of wall candidate clusters that were not determined to be non-wall points in step S59 as the processing result of the area non-wall surface removal process. (Step S60).

FIG. 22 is an example of a flowchart of the optimal wall surface selection process executed in step S7 of FIG.

First, the quay detection unit 15 obtains a group of wall candidate clusters, which are the processing results of the area non-wall surface removal process, as input for the optimal wall surface selection process (step S61). Next, the quay detection unit 15 calculates the representative position of each wall candidate cluster (step S62). Then, the quay detection unit 15 assigns a non-wall label to each point of the wall candidate cluster whose representative position is higher than the upper limit of the upper surface existence range, and to each point of the wall candidate cluster whose representative position is lower than the lower limit. (Step S63).

Then, the quay detection unit 15 determines whether there is one or more wall surface candidate clusters consisting of upper surface points (step S64). If there is one or more wall surface candidate clusters made up of top surface points (step S64; Yes), the quay detection unit 15 leaves the cluster with the lowest representative position among the wall surface candidate clusters made of top surface points, and removes the cluster from other clusters. A non-wall surface label is given to each point (step S65). On the other hand, if there is one wall candidate cluster consisting of upper surface points (step S64; No), step S65 is not executed.

Then, the quay detection unit 15 assigns a non-wall label to each point of the cluster, which is higher (that is, has a larger Z coordinate) than the representative point of the wall candidate cluster of the remaining top point among the wall candidate clusters made up of the side points. (Step S66). Then, the quay detection unit 15 leaves the cluster with the smallest absolute value of the Y coordinate (that is, close to the origin) among the wall surface candidate clusters of the remaining side points, and assigns a non-wall surface label to each point of the other clusters. (Step S67). Then, the quay detection unit 15 outputs a normal/wall labeled point group in which a normal and a wall label are associated for each measured point as a processing result of the optimal wall surface selection process (step S68).

FIG. 23 is an example of a flowchart of the boundary point determination process executed in step S8 of FIG.

First, the quay detection unit 15 obtains a group of points with normal/wall surface labels, which are the processing results of the optimal wall surface selection process, as an input for the boundary point determination process (step S71). Next, the quay detection unit 15 executes a processing loop of steps S72 to S75. In this case, the quay detection unit 15 sequentially selects all the vertical lines of the point cloud data as lines of interest, and repeats the loop until there are no more vertical lines to be selected as lines of interest.

First, the quay detection unit 15 searches for a non-wall point that exists between the side point and the top point (step S72). Then, the quay detection unit 15 calculates the distance from the nearest wall point (top point or side point) for each of the searched non-wall points (step S73). When the quay detecting unit 15 determines that there is a non-wall point for which the calculated distance is less than a predetermined threshold (step S74; Yes), the quay detecting unit 15 sets the wall label of the non-wall point to the nearest wall point. The same wall label is set (step S75). On the other hand, when the quay detection unit 15 determines that there is no non-wall point for which the calculated distance is less than the predetermined threshold (step S74; No), it does not execute the process of step S75.

After the vertical line loop processing is completed, the quay detection unit 15 outputs a group of wall labeled points in which wall labels are associated with each measured point as a processing result of the boundary point determination processing (step S76).

(10) Application example
Next, application examples (first application example to third application example) using the processing results of the flowchart shown in FIG. 3 will be described.

24 and 25 are diagrams showing an overview of the first application example. The first application example is quay detection when a ship docks at a quay having the shape shown in FIG. 24 . In this case, the lidar is attached to the side of the ship and illuminates the quay direction. Since the lidar beam is output radially, the distance between the acquired measurement points increases as the distance increases.

In the first application example, the first timing is when a point cloud of the quay begins to be acquired within the rider's field of view, the second timing is when a section where a protrusion exists on the quay wall is being detected, and the second timing is when the ship is close to the quay. The processing of this embodiment is executed at the third timing when the distance is reached. In this case, as shown in FIG. 25, when the process of this embodiment is performed at the first timing, the normal calculation radius is increased to search for a point, so the number of points used for normal calculation is reduced. prevent. Additionally, by being able to calculate normals from a long distance, the maximum distance at which quay walls can be detected is extended. Furthermore, when the process of this embodiment is performed at the second timing, protrusions can be removed using the normal dispersion threshold and only surfaces with high flatness can be extracted. Furthermore, after integrating the divided surfaces, wall surface determination can be performed based on area. Furthermore, when the process of this embodiment is performed at the third timing, the normal calculation radius is reduced and the point is searched for, so local normals are obtained and calculation accuracy is improved. In particular, the performance is improved at the boundary point between the top and side surfaces where the normal direction is likely to be disturbed.

26 and 27 are diagrams showing an overview of the second application example. In the second application example, a case is taken as an example in which an object other than a wall surface, such as a mooring rope or a buoy, exists within the field of view of the rider. Depending on the angle, these obstacle points may be labeled as top surface points or side surface points during normal line calculation, and may be mixed in with wall surface candidates. On the other hand, by executing the process of this embodiment, as shown in FIG. 27, outlying points can be removed by point filter processing (distributed non-wall surface removal processing, area non-wall surface removal processing, etc.). Furthermore, even if a wall label is erroneously assigned to an obstacle that cannot be removed based on the score threshold, it can be excluded from wall candidates by determination using area. As a result, as shown in FIG. 27, wall labels are applied seamlessly from the side surface to the top surface.

28 and 29 are diagrams showing an overview of the third application example. In the third application example, regarding the extraction of a quay with a complex shape, a case where the edge of the quay is not in a straight line and a plurality of wall surface candidates appear due to an obstacle on the quay is taken as an example. In this case, the top surface may be determined for the undertow. Furthermore, it is necessary to consider the distance to the opposite shore and preferentially determine wall candidate clusters that exist in positions close to the ship as wall points. In contrast, by executing the process according to this embodiment, as shown in FIG. This can reduce the number of events that occur. Furthermore, by determining that a more plausible surface is a quay, it is possible to improve the stability of calculation of the distance to the opposite shore (distance between the rider and the quay) using the quay detection results.

(11) Calculation of quay detection reliability
The berthing parameter calculation unit 16 may calculate the reliability of the quay detection result by the quay detection unit 15 (quay detection reliability) for each of the top surface and the side surface.

FIG. 30 is a table showing an example of calculating the quay detection reliability. Here, the coefficient "q ₃ " regarding "detection/undetection" is a value depending on whether the top surface or side surface is detected, and the coefficient "q ₂ " regarding "wall surface points" is a value depending on the size. This indicates whether the point cloud has been acquired, and the larger the value, the higher the reliability. The coefficient "q ₁ " related to "inside/outside the viewing angle" indicates whether the quay falls within the rider's viewing angle, and is 1 when the viewing margin is large and is within the viewing angle, -0.5 if there is a possibility that a quay exists. The coefficient “q ₀ ” related to “normal vector variance” represents the degree of variance of the normal vectors of clusters determined to be quays, and the smaller the variance, the higher the reliability. The side wall detection reliability " _qs " and the top wall detection reliability " _q.sub.u " are respectively calculated by weighted averages of the above-mentioned coefficients. Each of the weighting coefficients “w ₀ ” to “w ₃ ” used in this case is stored in the memory 12 or the like in advance, for example.

As described above, the controller 13 of the information processing device 1 mainly includes an acquisition means, a normal calculation range setting means, and a normal calculation means. The acquisition means acquires measurement data that is a collection of data representing a plurality of measured points measured by the measuring device. The normal line calculation range setting means sets a normal line calculation range for generating a point set of the measured points to be used for calculating the normal line of each of the measured points, based on the measurement distance indicated by the measurement data. The normal calculation means calculates the normal of each measured point based on the normal calculation range. With this aspect, the controller 13 can suitably calculate the normal line of each point to be measured measured by the measuring device.

In the embodiments described above, the program can be stored using various types of non-transitory computer readable media and supplied to a computer, such as a controller. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic storage media (eg, flexible disks, magnetic tape, hard disk drives), magneto-optical storage media (eg, magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, Includes CD-R/W, semiconductor memory (for example, mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, and RAM (Random Access Memory).

Although the present invention has been described above with reference to Examples, the present invention is not limited to the above-mentioned Examples. The configuration and details of the present invention can be modified in various ways that can be understood by those skilled in the art within the scope of the present invention. That is, it goes without saying that the present invention includes the entire disclosure including the claims and various modifications and modifications that a person skilled in the art would be able to make in accordance with the technical idea. In addition, the disclosures of the above cited patent documents, etc. are incorporated into this document by reference.

1 Information processing device 2 Sensor group 3 Lidar

Claims (11)

acquisition means for acquiring measurement data that is a collection of data representing a plurality of measured points measured by the measurement device;
normal line calculation range setting means for setting a normal line calculation range for generating a point set of the measured points to be used for calculating the normal line of each of the measured points, based on the measurement distance indicated by the measurement data;
normal line calculation means for calculating the normal line of each of the measured points based on the normal line calculation range;
An information processing device having: The information processing apparatus according to claim 1, wherein the normal line calculation range setting means increases the normal line calculation range as the measured distance is longer. clustering means for clustering the measured points based on the normal;
determining means for determining whether each of the clusters of measured points formed by the clustering is a quay candidate based on the degree of variation in the normal line within the cluster;
The information processing device according to claim 1 or 2, comprising: clustering means for clustering the measured points based on the normal;
determining means for estimating a surface that approximates each of the clusters of the measured points formed by the clustering, and determining whether or not the clusters need to be integrated based on the distance between the surfaces;
The information processing device according to any one of claims 1 to 3, comprising: clustering means for clustering the measured points based on the normal;
determining means for determining whether each of the clusters of the measured points formed by the clustering is a quay candidate based on the number of the measured points forming the cluster and the area of the cluster;
The information processing device according to any one of claims 1 to 4. clustering means for clustering the measured points based on the normal;
Among the clusters of the measured points that are candidates for the top surface of the quay formed by the clustering, a cluster that exists within a predetermined existence range and is located at the lowest position is determined to be a cluster representing the top surface. a means for determining,
The information processing device according to any one of claims 1 to 5. The determining means is configured to select one of the clusters of measured points that are candidates for the side surface of the quay, which are located at a lower position than the cluster representing the top surface, and which is closest to the measuring device. The information processing device according to claim 6, wherein an existing cluster is determined to be a cluster representing the side surface. Based on the normal line, each of the measured points has a classification means for classifying whether it is a wall point where a quay is measured or a non-wall point where a part other than the quay is measured,
Any one of claims 1 to 7, wherein the classification means reclassifies the non-wall point existing between the wall points as the wall point based on the shortest distance between the non-wall point and the wall point. The information processing device according to item 1. A control method executed by a computer,
Obtain measurement data, which is a collection of data representing multiple measured points measured by a measuring device,
Based on the measurement distance indicated by the measurement data, setting a normal calculation range for generating a point set of the measurement points used for calculating the normal of each of the measurement points,
calculating a normal to each of the measured points based on the normal calculation range;
Control method. Obtain measurement data, which is a collection of data representing multiple measured points measured by a measuring device,
Based on the measurement distance indicated by the measurement data, setting a normal calculation range for generating a point set of the measurement points used for calculating the normal of each of the measurement points,
A program that causes a computer to execute a process of calculating a normal line of each of the measured points based on the normal line calculation range. A storage medium storing the program according to claim 10.