DETECTION AREA SETTING DEVICE, OBSTACLE DETECTION SYSTEM, AND DETECTION AREA SETTING METHOD

Abstract

To provide a detection area setting device that can update a detection area accurately and easily even when a reference measured object serving as a setting object of a detection area changes.SOLUTION: A detection area setting device comprises: a point group data acquisition unit that acquires three-dimensional point group data including a reflection point on a surface of the reference measured object on the basis of reflection light with respect to measurement light with which an area including a reference measured object is irradiated; a face acquisition unit that acquires a plurality of faces including the surface of the reference measured object on the basis of the point group data; a labelling part that labels the plurality of acquired faces under a prescribed condition; and an area setting unit that sets a detection area for detecting an obstacle on the basis of the plurality of labelled faces. The face acquisition unit is configured to implement processing for acquiring a normal to an attention surface to be generated at the attention point and a vicinity point of the attention point of a plurality of distance measurement points included in the point group data as a normal vector at the attention point with respect to a plurality of attention points, and acquires the plurality of faces including any of each attention point on the basis of each normal vector.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a detection area setting device, an obstacle detection system, and a detection area setting method. [Background technology]

[0002] Patent document 1 discloses a movable home gate device that aims to improve the safety of train operations by updating the detection area for obstacle detection by the detector of the movable home gate device for each train that stops at the platform.

[0003] The movable home gate device has a fence installed along the edge of the tracks on the platform, and the portion facing the side door when the train is stopped is an opening that can be opened and closed, and includes a detection device installed for each opening, whose detection area includes the area between the outside of the fence and the train stopped at the platform, and a control means for the detection device, and the control means is equipped with a detection area update function that updates the detection area of ​​the detection device in response to the position and shape of the side of the vehicle for each train stopped at the platform or for each car that makes up the train formation.

[0004] That is, the control means measures the distance from the irradiation starting point of the detection device to each part of the train's vehicle side, and identifies the shape and position of the train's vehicle side from the measured distance to each part of the vehicle side.The control means then determines a detection area for detecting obstacles for the train currently parked at the platform to a range that corresponds to the identified shape and position of the vehicle side.Specifically, the control means determines the detection area so that a surface having a shape along the vehicle side within a range included in the side shape detection range is formed on the platform side in the train width direction at a position a predetermined distance away from the vehicle side, and updates it as the detection area for detecting obstacles for the detection device.

[0005] As a detection device, a LiDAR (Light Detection and Ranging) device is used, which measures the distance to an object by irradiating the object with a scanning laser light and measuring the scattered and reflected light. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2013-203118 A Summary of the Invention [Problem to be solved by the invention]

[0007] The detection area updating method disclosed in Patent Document 1 updates a preset side shape detection range to a new side shape detection range that is a predetermined distance away from the detected vehicle side shape as a reference, and therefore has the following inherent problems.

[0008] In order to update the detection area, the control means had to obtain a shape at a predetermined distance from the shape of the vehicle side detected by the detection device as a reference, and therefore the control means had to execute very complicated and heavy-load calculations to calculate a shape at a uniform predetermined distance from the vehicle side based on the measured distances to each part of the vehicle side.

[0009] Furthermore, the side shape detection range needs to be preset, and the task of individually setting the range based on the positional relationship between the detection device and the object to be detected is extremely cumbersome and time-consuming.

[0010] Furthermore, when the control means detects the shape of the vehicle's side via the detection device, the laser light emitted from the detection device may pass through the window glass in the vehicle's door, and reflected light from other objects may be detected through the window glass, which creates the inherent problem that the control means cannot accurately detect the shape of the vehicle's side.

[0011] Similar problems occur not only in the detection device of the movable home gate device, but also in various other situations where the reference area for setting the detection area changes. For example, the detection area is set when a detection device detects obstacles in the vicinity of a vehicle parked on a lifter of a multi-storey parking lot, and the vehicle is moved to a storage facility if no obstacles are present. The detection area must be updated depending on the vehicle type and parking position, and similar problems exist.

[0012] In view of the above-mentioned conventional technology, an object of the present invention is to provide a detection area setting device, an obstacle detection system, and a detection area setting method that can accurately and easily update a detection area even when the reference measured object for which the detection area is set changes. [Means for solving the problem]

[0013] In order to achieve the above-mentioned object, a first characteristic configuration of the detection area setting device according to the present invention is configured to include a point cloud data acquisition unit that acquires three-dimensional point cloud data indicating the coordinates of distance measurement points including reflection points on the surface of a reference object to be set as a detection area based on reflected light of measurement light irradiated onto an area including a reference object to be set as a detection area, a surface acquisition unit that acquires multiple surfaces including the surface of the reference object based on the point cloud data acquired by the point cloud data acquisition unit, a labeling unit that labels the multiple surfaces acquired by the surface acquisition unit under predetermined conditions, and an area setting unit that sets the detection area for detecting obstacles based on the multiple surfaces labeled by the labeling unit, wherein the surface acquisition unit executes a process for acquiring, for the multiple attention points, a normal to a surface of interest generated at a point of interest and a point near the point of interest among the multiple distance measurement points included in the point cloud data as a normal vector at the point of interest, and executes a surface acquisition process to acquire multiple surfaces including any of the points of interest based on each normal vector.

[0014] Based on the three-dimensional point cloud data indicating the coordinates of the distance measurement points relative to the area including the reference object acquired by the point cloud data acquisition unit, a plurality of surfaces including the surface of the reference object are acquired by the surface acquisition unit. A detection area for detecting an obstacle is set by the area setting unit based on the plurality of surfaces labeled by the labeling unit.

[0015] In other words, since the detection area can be defined based on multiple labeled surfaces rather than distance data from individual ranging points, the detection area can be updated accurately and easily even if the shape or position of the reference measured object contained in the reference area for setting the detection area changes.

[0016] Then, the surface acquisition unit executes a surface acquisition process. In the surface acquisition process, a surface of interest is generated at a point of interest and a plurality of points adjacent to the point of interest among a plurality of distance measurement points included in the point cloud data, and a normal to the surface of interest is acquired as a normal vector at the point of interest. In the surface acquisition process, the same process is repeated for the plurality of points of interest. This makes it possible to easily acquire a plurality of surfaces including any of the points of interest based on each normal vector.

[0017] The second characteristic configuration is that, in addition to the first characteristic configuration described above, the surface acquisition process includes a clustering process in which, if the normal vectors of other points of interest that exist within a predetermined search range from a specific point of interest fall within a predetermined allowable range of inclination with respect to the direction of the normal vector of the specific point of interest, the other points of interest are grouped together with the specific point of interest.

[0018] In the clustering process, other points of interest that exist within a predetermined search range are extracted based on a specific point of interest, and when the inclination of the normal vector of the other points of interest relative to the normal vector of the specific point of interest falls within a predetermined allowable range, the other points of interest are grouped in the same group as the specific point of interest as candidate points that constitute the same surface as the surface that includes the specific point of interest. The search range is a value that is appropriately set according to the reference object to be measured. For example, in the case where the reference object to be measured is the side of a train, the search range is set to a value that excludes points of interest in an area far away from the side of the train, while causing a plurality of points of interest corresponding to both sides of the train's window area to belong to the same group.

[0019] The third characteristic configuration is that, in addition to the second characteristic configuration described above, the surface acquisition process repeats the clustering process with each focus point as the specific focus point, and if any of the focus points to be grouped in a later clustering process have already been grouped in a previous clustering process, the surface acquisition process includes an integration process that integrates all of the focus points to be grouped in the later clustering process into the same group as the previous clustering process.

[0020] When the clustering process is repeated with each attention point as a specific attention point, a cluster including other attention points existing on the same surface is formed for each specific attention point. When any of the attention points constituting each cluster belongs to any of the previously formed clusters, the points are integrated into the same cluster as the previously formed cluster, thereby increasing the number of attention points that are candidates to be included in the same surface. In other words, the surface generated in the specified search range is expanded beyond the specified search range.

[0021] The fourth characteristic configuration is that, in addition to the second or third characteristic configuration described above, the surface acquisition process includes an integration process in which a representative normal vector representing each of the multiple groups generated by the clustering process is obtained, and groups whose respective representative normal vectors fall within a predetermined tolerance range are integrated into the same group.

[0022] The attention points grouped by the second or third characteristic configuration are a set of a plurality of attention points existing in a predetermined search range based on at least a specific attention point. If the search range is set large, attention points that originally belong to different faces may belong to the same group, so the search range must be limited to a certain size. However, if the search range is set small, attention points that originally belong to the same face may belong to different groups. Therefore, in this configuration, when the representative normal vectors of each group fall within a predetermined allowable range, the representative normal vectors are integrated into the same group. The predetermined allowable range can be defined, for example, by the orientation of the representative normal vector, the distance between the starting points of the representative normal vectors relative to a predetermined reference plane, or the like.

[0023] The fifth characteristic configuration is that, in addition to any one of the second to fourth characteristic configurations described above, the surface acquisition process performs a filtering process on the points of interest grouped into the same group to remove outliers from the same group.

[0024] The point cloud data grouped into the same group by the clustering process is a collection of attention points whose distance between each ranging point and the inclination of the normal direction are within a predetermined allowable range, and the distance of each attention point from the normal direction of a specific attention point or the tangent plane of a specific attention point is not evaluated. Therefore, by performing a filtering process, outliers that cannot be evaluated as existing on the same plane are appropriately removed.

[0025] A sixth characteristic configuration of the present invention is that, in addition to the above-mentioned fifth characteristic configuration, the filtering process is a process for executing robust estimation including any one of RANSAC, M-estimation, or least median method.

[0026] Robust estimation can be preferably used as such filtering processing. Robust estimation is a statistical processing method that aims to evaluate the possibility that a given observation value contains an outlier and suppress its influence, and specifically, any of RANSAC, M-estimation, and least median methods can be preferably used.

[0027] A seventh characteristic feature of the present invention is that, in addition to any one of the first to sixth characteristic features described above, the surface acquisition process acquires a surface based on attention points included in the same group.

[0028] The eighth characteristic configuration of the same is that, in addition to any of the first to seventh characteristic configurations described above, the labeling unit identifies and labels a reference plane corresponding to the surface of the reference object from the multiple surfaces and an intersecting plane that intersects the reference plane based on a preset reference vector and the normal vectors of the multiple surfaces acquired by the surface acquisition unit.

[0029] The labeling section identifies a surface whose normal vector has a predetermined relationship with a preset reference vector, specifically a reference surface corresponding to the surface of a reference object to be measured, and an intersecting surface that intersects with the reference surface, and labels each of these as two surfaces that define a detection area.

[0030] The ninth characteristic configuration is, in addition to any of the first to seventh characteristic configurations described above, that the labeling unit identifies a reference surface corresponding to the surface of the reference object from the multiple surfaces based on a preset reference vector and the normal vectors of the multiple surfaces acquired by the surface acquisition unit, acquires a boundary line of the reference surface based on the point cloud data acquired by the point cloud data acquisition unit, and generates an intersection surface that passes through the boundary line and intersects with the reference surface, and identifies and labels the reference surface and the intersection surface that intersects with the reference surface.

[0031] The labeling unit identifies a surface whose normal vector has a predetermined relationship with a preset reference vector, specifically a reference surface corresponding to the surface of a reference object to be measured, and further acquires a boundary line of the reference surface based on point cloud data including the reference surface, generates an intersecting surface that passes through the boundary line and intersects with the reference surface, and labels the reference surface and the intersecting surface that intersects with the reference surface so that they can be identified as two surfaces that define a detection area.

[0032] The tenth characteristic feature of the present invention is as described above. Eighth or NinthIn addition to the characteristic configuration described above, the area setting unit defines a detection area consisting of a polyhedron between the reference surface and the source of the measurement light based on the reference surface and the intersection surface labeled by the labeling unit.

[0033] A polyhedron that serves as a detection area is defined on the side of the irradiation source of the measurement light, based on the labeled reference plane and the intersection plane.

[0034] A first characteristic configuration of the obstacle detection system according to the present invention is that it comprises a detection area setting device having any of the first to tenth characteristic configurations described above, and a range sensor that scans a measurement light in an area including a detection area set by the detection area setting device and determines the presence or absence of an obstacle based on the presence or absence of reflected light from the detection area.

[0035] The range sensor determines whether or not an obstacle is present in the detection area defined by the detection area setting device.

[0036] A first characteristic configuration of the detection area setting method according to the present invention comprises a point cloud data acquisition step of acquiring three-dimensional point cloud data indicating the coordinates of distance measurement points including reflection points on the surface of a reference object based on reflected light of measurement light irradiated onto an area including a reference object to be set as a detection area, a surface acquisition step of acquiring multiple surfaces including the surface of the reference object based on the point cloud data acquired in the point cloud data acquisition step, a labeling step of labeling the multiple surfaces acquired in the surface acquisition step under predetermined conditions, and an area setting step of setting the detection area for detecting obstacles based on the multiple surfaces labeled in the labeling step, wherein the surface acquisition step executes a process for acquiring, for multiple attention points, a normal to a surface of interest generated at a point of interest and a point near the point of interest among the multiple distance measurement points included in the point cloud data as a normal vector at the point of interest, and acquires multiple surfaces constituted by any of the points of interest based on each normal vector.

[0037] The second characteristic configuration is, in addition to the first characteristic configuration described above, that the surface acquisition step includes executing a clustering process in which, if the normal vectors of other points of interest that exist within a predetermined search range from the specific point of interest fall within a predetermined allowable range of inclination with respect to the orientation of the normal vector of the specific point of interest, the other points of interest are grouped together with the specific point of interest.

[0038] The third characteristic configuration is, in addition to the second characteristic configuration described above, that the surface acquisition step includes executing an integration process in which the clustering process is repeated with each focus point as the specific focus point, and if any of the focus points to be grouped in a later clustering process have already been grouped in a previous clustering process, all of the focus points to be grouped in the later clustering process are integrated into the same group as the previous clustering process.

[0039] The fourth characteristic configuration is that, in addition to the second or third characteristic configuration described above, the surface acquisition step includes executing an integration process to obtain a representative normal vector representing each of the multiple groups generated by the clustering process, and to integrate into the same group those groups whose orientations of the respective representative normal vectors fall within the predetermined tolerance range.

[0040] The fifth characteristic configuration is, in addition to any one of the second to fourth characteristic configurations described above, that the surface acquisition step performs a filtering process on the points of interest that have been grouped into the same group in the clustering process to remove outliers from the same group.

[0041] A sixth characteristic configuration of the present invention is that, in addition to the above-mentioned fifth characteristic configuration, the filtering process is a process for executing robust estimation including any one of RANSAC, M-estimation, or least median method.

[0042] The seventh characteristic configuration is that, in addition to any one of the first to sixth characteristic configurations described above, the labeling step identifies and labels a reference plane corresponding to the surface of the reference object from the multiple surfaces and an intersecting plane that intersects the reference plane based on a preset reference vector and the normal vectors of the multiple surfaces acquired in the surface acquisition step.

[0043] The eighth characteristic configuration of the present invention is, in addition to any one of the first to sixth characteristic configurations described above, that the labeling step identifies a reference surface corresponding to a surface of the reference object from the multiple surfaces based on a preset reference vector and normal vectors of the multiple surfaces acquired in the surface acquisition step, acquires a boundary line of the reference surface based on the point cloud data acquired in the point cloud data acquisition step, generates an intersecting surface that passes through the boundary line and intersects with the reference surface, and identifies and labels the reference surface and the intersecting surface that intersects with the reference surface.

[0044] The ninth characteristic configuration of the present invention is as described above. Seventh or Eighth In addition to the characteristic configuration described above, the area setting step defines a detection area consisting of a polyhedron between the reference surface and the source of the measurement light based on the reference surface and the intersection surface labeled in the labeling step. Effect of the Invention

[0045] As described above, according to the present invention, it is possible to provide a detection area setting device, an obstacle detection system, and a detection area setting method that can accurately and easily update a detection area even when the reference measured object for which the detection area is set changes. [Brief description of the drawings]

[0046] [Figure 1] (a) is an explanatory diagram of a side view of the obstacle detection area defined between the train and the platform safety fence, (b) is an explanatory diagram of a plan view of the same detection area, and (c) is an explanatory diagram overlooking the same detection area. [Diagram 2](a) is an explanatory diagram of the functional blocks of the obstacle detection system, and (b) is an explanatory diagram of the LIDAR. [Diagram 3] FIG. 4A is an explanatory diagram of a detection area setting device (setting unit), and FIG. 4B is a flowchart showing a procedure for detection area setting processing. [Figure 4] 13 is a flowchart showing the procedure of a surface acquisition process. [Diagram 5] 13 is a flowchart showing a procedure of a labeling process. [Figure 6] 13 is a flowchart showing a procedure for a detection area setting process. [Figure 7] 13 is a flowchart showing a procedure for an obstacle detection process. [Figure 8] 11A, 11B, and 11C are explanatory diagrams of a surface acquisition process. [Figure 9] FIG. 11 is an explanatory diagram of a first step of the detection area setting process. [Figure 10] FIG. 11 is an explanatory diagram of a second step of the detection area setting process. [Figure 11] FIG. 11 is an explanatory diagram of a third step of the detection area setting process. [Figure 12] 13 is a flowchart showing the procedures of a surface acquisition process, a labeling process, and a general-purpose detection area setting process. [Figure 13] 13A and 13B are explanatory diagrams of a surface acquisition process for setting a general-purpose detection area. [Figure 14] FIG. 11 is an explanatory diagram of a platform edge recognition process. [Figure 15] 11A, 11B, and 11C are diagrams illustrating a general-purpose detection area setting process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0047] An example of a detection area setting device, an obstacle detection system, and a detection area setting method according to the present invention will be described below.

[0048] [Detection area set on train platform] As shown in Fig. 1(a), (b), and (c), a safety fence SF is installed on the edge of a platform P installed in a railway station building on the track side to prevent accidents such as passengers coming into contact with a train. The safety fence SF is composed of a fence body SB with a gate installed at a position corresponding to the door 2 of the vehicle 1, and a door DR for opening and closing the gate. The X, Y, and Z coordinate systems shown in each figure indicate three-dimensional coordinate systems with the installation position of any one of the riders 10 as the origin. Fig. 1(a) shows the positional relationship between the vehicle 1, the platform P, and the safety fence SF as viewed along the Y-axis direction. Fig. 1(b) shows the positional relationship between the vehicle 1, the platform P, and the safety fence SF as viewed from above along the Z-axis direction.

[0049] A LiDAR (Light Detection and Ranging) 10 capable of two-dimensional scanning so as to overlook the space around the door 2 of the vehicle 1 is installed above the door DR on the fence body SB.

[0050] As shown in Figure 2(b), the LIDAR 10 adopts a TOF method and comprises a cylindrical casing 11 with an optical window W, a light projecting and receiving unit 13 housed in the casing 11 and equipped with a laser light source consisting of a semiconductor laser and a light receiving element, a scanning optical system 14, and a signal processing circuit 12 that controls them.

[0051] The scanning optical system 14 includes a deflection mirror M. The scanning optical system 14 also includes a first deflection mechanism 14A that oscillates the deflection mirror M about a first axis P1 that is perpendicular to the paper surface of FIG. 2(b). The first deflection mechanism 14A includes an electromagnetic oscillation mechanism. The scanning optical system 14 also includes a motor that rotates the second deflection mechanism 14B about a second axis P2 that is parallel to the paper surface of FIG. 2(b).

[0052] The scanning optical system 14 repeatedly scans the horizontal scanning line hd in a vertical direction vd via the first deflection mechanism 14A while repeatedly scanning the pulsed measurement light emitted from the laser light source in a horizontal direction hd within a predetermined range via the second deflection mechanism 14B. The signal processing circuit calculates the distance and direction to the reflection point of the measurement light based on the time difference between the emission timing of the measurement light and the detection timing of the reflected light. That is, the LIDAR 10 detects the presence or absence of an obstacle based on the reflected light from an object present in the space surrounding the door 2 of the vehicle 1.

[0053] In the present invention, an obstacle detection area DA is set in the space scanned by the measurement light so as not to erroneously determine that the platform P or the vehicle 1 is an obstacle based on the light reflected from the surface of the platform P or the vehicle 1. In the obstacle detection process in the obstacle detection system, which will be described later in detail using the flowchart of FIG. 7, an object present within the detection area DA is detected as an obstacle based only on the light reflected from the object. As a result, passengers 3 getting on and off the vehicle 1 and baggage carried by the passengers 3 are detected as obstacles. When no obstacle is detected, the door 2 of the vehicle 1 and the door DR of the gate are safely closed.

[0054] The detection area DA is a region partitioned into a substantially rectangular parallelepiped shape having a predetermined width W set in the left-right direction along the above-mentioned side of the vehicle 1 based on the position of the rider 10, at a predetermined depth D set inside the platform P, based on an offset surface that is a predetermined height ΔH from the floor surface of the platform P and along the traveling direction of the vehicle 1, and that is spaced a predetermined width ΔD from the side close to the platform P. The detection area DA is an individual detection area that is set each time the vehicle 1 enters the platform P, because the position of the side of the vehicle facing the platform P differs depending on the model of the vehicle 1, etc.

[0055] [Configuration of Obstacle Detection System] As shown in FIG. 2(a), the obstacle detection system 100 includes a detection area setting unit 20 that sets a detection area in which the presence or absence of an obstacle is determined, a range sensor that scans the detection area set by the detection area setting unit 20 with measurement light and determines the presence or absence of an obstacle based on the presence or absence of reflected light from the detection area, and a system control unit 50 that controls the operation timing of the detection area setting unit 20 and the range sensor.

[0056] The LIDAR 10, which is equipped with a laser light source, a light receiving element, a scanning optical system, and a signal processing circuit 12, the detection area information storage unit 30, and the obstacle detection unit 40 constitute a range sensor, and the detection area setting device of the present invention is constituted by the detection area setting unit 20. The detection area information storage unit 30 stores and manages the detection area. The obstacle detection unit 40 determines the presence or absence of an obstacle in the detection area, and outputs information on the presence or absence of an obstacle.

[0057] The signal processing circuit 12 includes a PLC (programmable logic controller) that controls the laser light source, the light receiving element, and the scanning optical system, a dedicated DSP (digital signal processor) that performs distance measurement calculations on the reflected light detected by the light receiving element, etc. In this embodiment, an FPGA (Field Programmable Gate Array) is used as the signal processing circuit 12.

[0058] The detection area information storage unit 30 is composed of a semiconductor memory, and the detection area setting unit 20 and the obstacle detection unit 40 are each composed of a microcomputer and a semiconductor memory in which an application program executed by the microcomputer is stored, an input / output circuit, a communication circuit, etc.

[0059] The system control unit 50 is composed of a general-purpose computer installed with application programs for operation management that perform processing for controlling the opening and closing of the door DR of the safety fence SF, processing for detecting the stoppage of the vehicle 1, processing for detecting the open / closed state of the vehicle door 2, and processing for determining whether or not the vehicle 1 can depart.

[0060] As shown in FIG. 7, when vehicle 1 has not entered platform P, the system control unit 50 sets a general detection area that is independent of the model of vehicle 1, etc. (SD1), detects whether or not an obstacle is present within the general detection area via the obstacle detection unit 40, and if it determines that an obstacle is present (SD2, present), outputs an obstacle detection signal (obstacle present) to an external system such as an alarm system (SD3). If no obstacle is detected (SD2, no), the system control unit 50 proceeds to detecting vehicle 1 entering the platform in step SD4 without outputting an obstacle detection signal (obstacle present).

[0061] The general-purpose detection area is the smallest detection area set based on the type of vehicle in which the side of the vehicle 1 facing the platform P is closest to the lidar 10. This general-purpose detection area is defined as an initial setting when the system is installed, etc. In addition, it is desirable for the general-purpose detection area to be the smallest detection area among the individual detection areas described in detail later, or a detection area smaller than that.

[0062] The system control unit 50 repeats the obstacle detection process (SD2) (SD4, N) until it detects that the vehicle 1 has entered the platform P and stopped based on the input from the entrance detection sensor. When it detects that the vehicle 1 has entered the platform P and stopped (SD4, Y), it starts the detection area setting unit 20 in the closed state before the door 2 of the vehicle 1 is opened, and requests the detection area setting unit 20 to set a detection area for the vehicle 1 (SD5). An imaging device, a lidar, or the like can be used as the entrance detection sensor, and the function thereof can also be incorporated into the obstacle detection system 100. The detection area is set for each door DR provided in the safety fence SF. This is to detect passengers passing between the door DR and the door 2 of the vehicle 1.

[0063] When the system control unit 50 receives a signal from the detection area setting unit 20 indicating that the setting of the detection area has been completed (SD6, Y), it opens the gate door DR and the door 2 of the vehicle 1, and monitors the movement of passengers getting on and off within the detection area via the detection output of the obstacle detection unit 40 based on the detection area corresponding to the vehicle 1 (SD7).

[0064] The system control unit 50 acquires obstacle detection information via the obstacle detection unit 40. When the system control unit 50 determines that an obstacle is present (SD8, present), it outputs an obstacle detection signal (obstacle present) to an external system or the like (SD11). On the other hand, when the system control unit 50 determines that no obstacle is present (SD8, no), it returns to step SD7 and continues the process of repeating the monitoring of passengers getting on and off for the allowable stopping time of the vehicle 1 without outputting the obstacle detection signal (obstacle present).

[0065] When it is time for the vehicle 1 to depart, the system control unit 50 judges whether or not the vehicle can depart (SD9). The judgment of whether or not the vehicle can depart is based on the criterion that there is no risk of a serious accident occurring due to the departure of the vehicle 1, such as that the doors 2 of the vehicle 1 are closed and that no passengers or baggage are detected within the detection area by the obstacle detection process.

[0066] If a passenger's body or bag is caught in the door 2, or if passengers getting on or off are detected within the detection area, the system control unit 50 reopens the door 2 of the vehicle 1. Alternatively, the system control unit 50 controls the vehicle to prohibit departure while keeping the gate door DR open, and repeats the processes from step SD7 onwards.

[0067] If no passengers or luggage are detected within the detection area, the system control unit 50 closes the gate door DR and allows the vehicle 1 to depart (SD9, OK). Then, the system control unit 50 switches the detection area to a general-purpose detection area (SD10). After that, the system control unit 50 repeats the processes from step SD2 onwards.

[0068] In this embodiment, an example has been described in which the system control unit 50 automatically controls the running of the vehicle 1 and the opening and closing of the door 2. However, in an embodiment in which the running of the vehicle 1 and the opening and closing of the door 2 are controlled by a driver of the vehicle 1, the system control unit 50 only needs to detect the stop of the vehicle 1 and the open / closed state of the door 2 with a dedicated sensor, and can be configured to output a departure prohibition signal or permission signal to the vehicle 1.

[0069] [Configuration of detection area setting device] 3(a) and (b), the detection area setting device, i.e., the detection area setting unit 20, includes the functional blocks of a point cloud data acquisition unit 21, a surface acquisition unit 22, a labeling unit 23, and an area setting unit 24. These functional blocks are embodied by executing the above-mentioned application program on a microcomputer.

[0070] The detection area setting unit 20 sets a detection area for each LIDAR 10 installed near the door DR of each gate based on a detection area setting request from the system control unit 50. The detection area setting unit 20 associates each set detection area with an ID previously set for the LIDAR 10 and stores it in the detection area information storage unit 30. Thereafter, the detection area setting unit 20 outputs a signal indicating that the setting of the detection area has been completed to the system control unit 50. This enables the obstacle detection unit 40 to identify an individual detection area for each LIDAR 10. Below, the process of setting the detection area for one LIDAR 10 will be described, but it goes without saying that a similar setting process is performed for each LIDAR 10.

[0071] The point cloud data acquisition unit 21 activates the LIDAR 10 and acquires distance image data including distances and directions of distance measurement points including reflection points on the surface of the reference object based on the reflected light of the measurement light irradiated onto an area including the reference object to be set as the detection area.The point cloud data acquisition unit 21 then executes a point cloud data acquisition step of acquiring three-dimensional point cloud data, which is the three-dimensional coordinates of the distance measurement points, by arithmetic processing based on the distance image data (FIG. 3(b), S1).

[0072] In this embodiment, the detection area is an area defined between a safety fence SF provided on a platform P of a station building and a vehicle 1 parked on the platform P. Furthermore, the "reference object to be measured for which a detection area is to be set" refers to the vehicle 1 and the platform P, and the "area including the reference object to be set" refers to an area including the vehicle 1 and the platform P and including no obstacles such as passengers 3 getting on and off or baggage. Note that in the point cloud data acquisition step, the presence of only a small number of obstacles in the area is acceptable.

[0073] An example of the point cloud data obtained at this time is displayed as dots in Fig. 8(a). To facilitate understanding, in Fig. 8(a), auxiliary lines in the form of two-dot chain lines are added to the side of the vehicle 1 and the area corresponding to the floor surface of the platform P.

[0074] The scanning range of the measurement light by the LIDAR 10 is set to be at least wider than the detection area to be set.

[0075] The surface acquisition unit 22 executes a surface acquisition step to acquire multiple surfaces including the surface of the reference object, specifically the side surface of the vehicle 1 and the floor surface of the platform P, based on the point cloud data acquired by the point cloud data acquisition unit 21 (Figure 3(b), S2).

[0076] The labeling unit 23 executes a labeling step of labeling the multiple surfaces acquired by the surface acquisition unit 22 under predetermined conditions (FIG. 3(b), S3).

[0077] The area setting unit 24 executes an area setting step of setting a detection area for detecting an obstacle based on the multiple faces labeled by the labeling unit 23 (FIG. 3(b), S4).

[0078] Each step is described in detail below. Each time a unit scan in the horizontal and vertical directions is completed for an area including a reference object to be set as a detection area, the LIDAR 10 outputs direction and distance data to the reflection points to the point cloud data acquisition unit 21 via the signal processing circuit 12. When the point cloud data acquisition unit 21 receives this information from the signal processing circuit 12, it stores the point cloud data converted into coordinates in a three-dimensional coordinate space of X, Y, and Z of each reflection point together with a time stamp in an internal storage unit. The three-dimensional coordinate space may be a relative coordinate system with the installation position of each LIDAR 10 as the origin, or may be an absolute coordinate system that is not dependent on the installation position of each LIDAR 10. The point cloud data is not limited to three-dimensional coordinates of X, Y, and Z, and polar coordinates may also be adopted.

[0079] 4 shows the process executed by the plane acquisition unit 22. The plane acquisition unit 22 extracts a point of interest and a point adjacent to the point of interest from a plurality of distance measurement points included in the point cloud data, applies principal component analysis (PCA) to these extracted points to generate a plane of interest (SA1), and executes a process (SA2) for acquiring a normal vector to the plane of interest as a normal vector at the point of interest for all the point cloud data (SA3).

[0080] The point of interest refers to point cloud data selected based on a predetermined rule in order to obtain a normal vector from all point cloud data. For example, a thinning rate can be set as the rule. When the thinning rate is set to 50%, each set of point cloud data extracted every other point cloud data in the horizontal and vertical directions from all point cloud data obtained by unit scans in the horizontal and vertical directions becomes a point of interest. The thinning rate is a value that is appropriately set based on the number of all point cloud data, taking into consideration the computation load. Alternatively, each point cloud data whose distance is distributed in a predetermined range from among all point cloud data may be set as a point of interest. A rule may be defined by combining both such distance and thinning rate.

[0081] The plane acquisition unit 22 searches for distance measurement points existing within a radius of about several mm as neighboring points centered on the point of interest using the kd-tree method. The plane acquisition unit 22 calculates the center of gravity of the searched point cloud data, and then performs principal component analysis of the point cloud data. The plane acquisition unit 22 uses the third principal component vector obtained by the principal component analysis as a normal vector (a basis vector with a magnitude of 1) and calculates a plane passing through the center of gravity of the point cloud data previously obtained. FIG. 8(b) shows the normal vector obtained for the point cloud data corresponding to FIG. 8(a). Note that the point cloud data shown in FIGS. 8(b) and (c) is data viewed from an angle different from that of FIG. 8(a).

[0082] In addition to the above-mentioned principal component analysis method, methods for finding a surface from a point of interest and nearby points include the three-dimensional Hough transform method, the least squares method, the least median method, etc. For example, the least squares method calculates a plane that passes through the center of gravity of each point cloud data and minimizes the squared distance of each point cloud data.

[0083] Next, the surface acquisition unit 22 executes a clustering process. This clustering process includes the following steps SA4 to SA10. In the clustering process, first, a specific attention point is extracted from a plurality of attention points, and other attention points existing in a predetermined search range are extracted from the extracted specific attention point (SA4). Then, the specific attention point is compared with the normal vectors of the other attention points (SA5). If the normal vectors of the other attention points fall within a predetermined allowable range of inclination with respect to the direction of the normal vector of the specific attention point (SA6), a clustering process is executed to group the other attention points with the specific attention point (SA7). As candidate points constituting the same surface as the surface containing the specific attention point, the other attention points are grouped in the same loop as the specific attention point. In this embodiment, the inclination of the predetermined allowable range is set to 1°. The inclination of the predetermined allowable range is a value that can be appropriately set according to the object and the like. In addition, when grouping, the attention point and the distance measurement point that is a neighboring point of the attention point used to calculate the normal vector of the attention point may be grouped together, or only the attention point may be grouped. In the latter case, the normal vector may be included as an attribute value of the attention point.

[0084] The surface acquisition unit 22 repeats the clustering process with each attention point as a specific attention point (SA4 to SA10). If any of the attention points to be grouped in the later clustering process has already been grouped in the previous clustering process (SA8, Y), the surface acquisition unit 22 executes an integration process to integrate all the attention points to be grouped in the later clustering process into the same group as the previous clustering process (SA9).

[0085] In this embodiment, a space with a radius of about 2 m centered on a specific point of interest is set as the predetermined search range. For example, there are few measurement points above and below the window frame of the door 2 of the vehicle 1, and the estimation accuracy of normals is low, so there is a risk that the left and right side surfaces of the vehicle 1, sandwiched between the window, will be clustered as separate surfaces. Even in such a case, by setting a space with a radius of about 2 m as the search range, the left and right side surfaces of the vehicle 1, sandwiched between the window, will be clustered as the same surface. The value of the predetermined search range can also be set appropriately depending on the target object, etc.

[0086] Furthermore, after executing the integration process in step SA9, the surface acquisition unit 22 executes a second integration process (SA10'). The surface acquisition process obtains a representative normal vector representing each of the multiple groups generated by the clustering processes in steps SA4 to SA10, and integrates groups whose respective representative normal vectors fall within a predetermined allowable range into the same group.

[0087] The points of interest grouped by the clustering process of steps SA4 to SA10 are a set of a plurality of points of interest existing in a predetermined search range based on at least a specific point of interest. If the search range is set to a large value, points of interest that originally belong to different groups will end up in the same group, so the search range must be limited to a certain size, for example, a space with a radius of about 2 m as described above. If the search range is set to a smaller value than this, points of interest that originally belong to the same group may end up in different groups. Even in such a case, the points of interest can be integrated into the same group on condition that the representative normal vectors of each group fall within a predetermined allowable range.

[0088] A vector that is the median or average of the directions of the normal vectors of the points of interest that belong to the same group can be used as the representative normal vector. An approximate plane may be generated from multiple points of interest that belong to the same group, and a basis vector perpendicular to the plane may be used as the representative normal vector.

[0089] The specified tolerance range can be defined, for example, by both conditions that the direction of the representative normal vector falls within a specified tolerance range, and that the distance between an approximate plane generated from multiple focus points belonging to the same group and an approximate plane generated from multiple focus points belonging to another group falls within a specified range.

[0090] The second merging process in step SA10' is optional, and is not essential if the search range is set to an appropriate value. In addition, the merging processes in steps SA8 and SA9 are also optional, and if the merging processes in steps SA8 and SA9 are not performed, the second merging process effectively merges groups that belong to the same surface.

[0091] If the search range is enlarged in steps SA8 and SA9, or the allowable range of the distance between faces is enlarged in step SA10', a problem occurs in that, if the condition that the normal vector is within a predetermined allowable range of inclination is satisfied, a point of interest that is far from a face that should be composed of points of interest included in the same group is integrated into the same group. If such an event occurs, for example, there is a risk that a distance measurement point based on light reflected by the inside wall of the vehicle after passing through a window glass will be included in the same group.

[0092] Therefore, the surface acquisition unit 22 performs a filtering process on the point cloud data grouped into the same group by the clustering process to remove outliers from the same group (SA11). This process appropriately removes distance measurement points based on light reflected by the inner wall of the vehicle after passing through the window glass. Here, an outlier is a value that has an abnormally large residual from an estimated true value among the obtained observation values.

[0093] As the filtering process, RANSAC is preferably used. In this embodiment, three points are randomly selected from the points of interest included in the same group, and the distance between the plane created with the three points and each of the other points is calculated. If there are a certain number (1 / 4 of the total) or more of points whose calculated distance is equal to or less than a predetermined threshold (0.1 m), the points of interest are left as candidates, excluding outliers that deviate from the threshold. The surface acquisition unit 22 executes this process a predetermined number of times, and uses the point with the largest number of points that are equal to or less than the threshold to calculate the equation of the plane of the cluster. Note that the number of points selected to create the above-mentioned plane, the threshold, etc. are not particularly limited, and can be set as appropriate.

[0094] As a filtering process for removing outliers, in addition to RANSAC, M-estimation or the least median method can be adopted, and it is preferable to adopt a process for performing robust estimation including these. In Fig. 8(c), the outliers removed from the cluster representing the floor surface of platform P are shown in the dashed circle.

[0095] Next, after performing a filtering process on each group, a predetermined number of faces are extracted in descending order of the number of distance measurement points (SA12). In this embodiment, the side of the vehicle 1 and the floor surface of the platform P are acquired by extracting two faces in descending order of the number of distance measurement points.

[0096] 5 shows the process executed by the labeling unit 23. The labeling unit 23 distinguishably labels a reference plane corresponding to the surface of the reference object from among the multiple surfaces and an intersecting plane intersecting the reference plane, based on a preset reference vector and normal vectors of the multiple surfaces acquired by the surface acquisition unit 22.

[0097] In this embodiment, a vertical base vector is set as the reference vector. One of the two faces extracted in step SA2, that is, a face whose normal vector is orthogonal to the reference vector and that runs along the side of the vehicle 1 (the side running along the traveling direction of the vehicle 1 and close to the platform P), is identified as the first face (SB1). The other face, that is, a face whose normal vector is parallel to the reference vector, is identified as the second face that runs along the floor surface of the platform P (SB2). In FIG. 9, the reference vector VB, the normal vector VT of the first face, the normal vector VP of the second face, the first face C1, and the second face C2 are respectively shown.

[0098] The labeling unit 23 determines that the normal vector is orthogonal when the inclination angle of the normal vector with respect to the reference vector is within the range of 90°±Δθ1, and determines that the normal vector is parallel when the inclination angle of the normal vector with respect to the reference vector is within the range of 0°±Δθ2. The values ​​of Δθ1 and θ2 are not particularly limited, but are set to Δθ1=20° and Δθ2=10° in this embodiment.

[0099] 6 shows the process executed by the area setting unit 24. The area setting unit 24 defines a detection area formed of a polyhedron on the side of the irradiation source of the measurement light (the side of the LIDAR 10) based on the reference surface (first surface) labeled by the labeling unit 23 and an intersecting surface (second surface) intersecting the reference surface (first surface). The explanation will be continued with reference to FIGS. 9 to 11.

[0100] First, the area setting unit 24 specifies the directions of the normal vectors VT and VP that have already been calculated for the first surface C1 and the second surface C2 (SC1). The area setting unit 24 calculates the vector product (cross product) VT×VP of both vectors VT and VP to calculate the intersection vector between the first surface C1 and the second surface C2 and obtain the intersection line Li (SC2).

[0101] Next, the area setting unit 24 sets a width W that is the widthwise boundary of the detection area in the left and right directions along the intersection line Li, based on the center point CP on the intersection line Li that is the shortest distance from the rider 10 (SC3).

[0102] Next, the area setting unit 24 sets a depth D that is the boundary in the depth direction of the detection area from the intersection line along the second surface (SC4), and sets a height H that is the boundary in the height direction from the second surface (SC5). As a result, as shown in Fig. 10, a rectangular surface S1 with a width W and height H is formed on the surface of the first surface C1, and a rectangular surface S2 with a width W and depth D is formed on the surface of the second surface C2.

[0103] Next, the area setting unit 24 generates four auxiliary surfaces: auxiliary surfaces S3 and S4 opposed to the rectangular surfaces S1 and S2, and auxiliary surfaces S5 and S6 perpendicular to the rectangular surfaces S1 and S2 (SC6).

[0104] For the reference detection area BA obtained in this manner, as shown in Fig. 11, the area setting unit 24 shifts the rectangular surface S1 corresponding to the side surface of the vehicle 1 in the depth direction by an offset amount ΔD. Also, the rectangular surface S2 corresponding to the floor surface of the platform P is shifted upward by an offset amount ΔH. The area setting unit 24 defines the rectangular parallelepiped region obtained in this manner as the final detection area DA (SC7). The area setting unit 24 stores the coordinates of each vertex indicating the detection area DA in the detection area information storage unit 30 (SC8).

[0105] In this way, by setting a detection area DA for each vehicle 1 that enters the platform P, it becomes possible to set a detection area DA with as few blind spots as possible in response to various vehicles 1 with different vehicle widths. Note that the offset values ​​ΔD and ΔH described above are set appropriately so as not to erroneously detect the side walls of the vehicle or the floor of the platform P as an obstacle. If the offset values ​​ΔD and ΔH are set large, the blind spots become large, and if the offset values ​​ΔD and ΔH are set small, the probability of erroneously detecting an obstacle increases.

[0106] In the above-described embodiment, the detection area setting unit 20 defines the reference detection area BA every time the vehicle 1 enters the platform P, and further defines the final detection area DA based on the offset values ​​ΔD and ΔH. However, the reference detection area BA may be defined only when the vehicle 1 first enters the platform, and the detection area may be set by using the data of the reference detection area BA when the vehicle 1 subsequently enters the platform. Specifically, based on the data of the reference detection area BA initially acquired for each of the rectangular surface S2 corresponding to the floor surface of the platform P and the auxiliary surfaces S3 to S6, the surface acquisition unit 22 acquires only the first surface corresponding to the side surface of the vehicle 1, and generates a new rectangular surface S1.

[0107] In the above embodiment, an example has been described in which the surface acquisition unit 22 extracts two surfaces in descending order of the number of distance measurement points (attention points), i.e., the surfaces corresponding to the side surface of the vehicle 1 and the floor surface of the platform P, but if measurable, the surface corresponding to the safety fence SF may also be extracted. In this case, the area setting unit 24 does not need to generate the auxiliary surface S3, and an offset toward the vehicle 1 is set on the surface corresponding to the safety fence SF. In addition, the shape of the detection area DA is not limited to a rectangular parallelepiped, and it may be configured as a hexahedron or even a polyhedron.

[0108] After the door 2 of the vehicle 1 is opened, the obstacle detection unit 40 determines whether the data for the distance measurement point output from the signal processing circuit 12 of the LIDAR 10 is data within the detection area DA, and if the data is within the detection area DA, it determines that an obstacle is present.

[0109] 2, the detection area setting unit 20, the detection area information storage unit 30, the obstacle detection unit 40, and the signal processing circuit 12 are configured separately from the LIDAR 10 and connected by communication lines. However, the signal processing circuit 12, the detection area setting unit 20, the detection area information storage unit 30, and the obstacle detection unit 40 may each be integrally assembled in the housing of the LIDAR 10, or only the signal processing circuit 12 may be assembled in the housing of the LIDAR 10. Alternatively, the signal processing circuit 12, the detection area information storage unit 30, and the obstacle detection unit 40 may be assembled in the housing of the LIDAR 10, and the detection area setting unit 20 may be configured separately.

[0110] In the above-described embodiment, an example was described in which when vehicle 1 is not entering platform P, the system control unit 50 sets a general-purpose detection area that is not dependent on the model of vehicle 1, etc., as the minimum detection area. However, the general-purpose detection area needs to be individually defined as an appropriate range as an initial setting when the system is installed, etc., and additional work such as preparatory work for this purpose is required.

[0111] Therefore, when the vehicle 1 has not entered the platform P, the system control unit 50 requests the detection area setting unit 20 to set a general-purpose detection area, and the general-purpose detection area is automatically generated by the detection area setting unit 20, thereby making it possible to eliminate the need for complicated preparatory work. Below, the setting process of the general-purpose detection area by the detection area setting unit 20 will be described in detail, focusing on the differences from the above-mentioned detection area setting process.

[0112] In this example, the "reference measured object for which the detection area is to be set" refers to the platform P, and the "area including the reference measured object for which the detection area is to be set" refers to the area that includes the platform P and is free of obstacles such as boarding and alighting passengers 3 or luggage, and includes the track surface of the vehicle 1, etc.

[0113] When the system control unit 50 receives an instruction to set a general-purpose detection area from the system control unit 50 while a vehicle 1 is not entering the platform P, the point cloud data acquisition unit 21, surface acquisition unit 22, labeling unit 23, and area setting unit 24 provided in the detection area setting unit 20 execute the setting process of the general-purpose detection area.

[0114] As shown in Figure 12, the point cloud data acquisition unit 21 acquires three-dimensional point cloud data indicating the coordinates of distance measurement points including reflection points on the surface of a reference object to be measured, i.e., a reference object to be set as a general detection area, based on the reflected light of measurement light irradiated onto an area including the platform P (SE1).

[0115] When point cloud data is acquired by the point cloud data acquisition unit 21, the surface acquisition unit 22 extracts a point of interest and points adjacent to the point of interest from multiple ranging points included in the point cloud data, applies principal component analysis to these extracted points to generate a surface of interest (SE2), and executes a process (SE3) for all point cloud data to acquire a normal vector to the surface of interest as a normal vector at the point of interest (SE4).

[0116] Next, the surface acquisition unit 22 executes clustering processing (SE5 to SE11). The clustering processing is similar to the clustering processing (SA4 to SA10) previously described based on FIG. 4, and therefore a description thereof will be omitted. However, since a situation does not occur in which the left and right sides of the vehicle 1 are clustered into the same surface across the window as in the previous example, the search range may be smaller than that in the previous example, and may be set to a space with a radius of about 0.1 m centered on a specific point of interest, for example. Furthermore, the surface acquisition unit 22 removes outliers from the same group by executing a filtering process on the point cloud data grouped into the same group in the clustering process (SE12).

[0117] Figure 13(a) shows point cloud data mainly corresponding to the platform P and the orbital plane detected by the lidar 10. Figure 13(b) shows normal vectors obtained for the point cloud data corresponding to Figure 13(a).

[0118] Continuing the explanation by returning to Fig. 12, the labeling unit 23 identifies a reference plane RP corresponding to the surface of the platform P, which is the reference object to be measured, from the multiple planes, based on a preset reference vector and the normal vectors of the multiple planes acquired by the plane acquisition unit 22 (SE13).

[0119] As a reference vector, a vertical base vector detected by an acceleration sensor built into the Rider 10 is set as the reference vector VB, and among a plurality of planes having normal vectors whose direction coincides with that of the reference vector VB, the plane having the closest vertical distance from the Rider 10 is identified as the reference plane RP. When the inclination angle of the normal vector with respect to the reference vector VB is within the range of 0°±Δθ2, it is determined to be parallel. The value of Δθ2 is not particularly limited, but is set to Δθ2=10° in this embodiment. Note that the setting of the reference vector is not limited to a mode using an acceleration sensor built into the Rider 10, and may be a mode in which the reference vector is input in advance as data corresponding to the installation posture of the Rider 10, for example.

[0120] Next, the labeling unit 23 acquires the boundary line BL of the reference plane RP based on the point cloud data acquired by the point cloud data acquisition unit 21 (SE14). As shown in FIG. 14, the labeling unit 23 captures the distances d0, d1, ···, dn, dn+1, ···, dm (n, m are integers) obtained along the scanning direction (indicated by arrows in FIG. 14) by the first deflection mechanism 14A of the laser light emitted from the lidar 10, and performs a process of obtaining boundary points (positions corresponding to the distance dn (dn << dn+1) in FIG. 14) where the values change abruptly. This process is repeated along the scanning direction by the second deflection mechanism 14B, and an approximate line passing through the obtained plurality of boundary points is acquired as the boundary line BL. As shown in FIG. 14, in the case of this embodiment, the boundary line BL to be acquired corresponds to the edge on the track surface side of the platform P which is the reference object to be measured. Since there is a height difference between the platform P and the track surface, a boundary where the value of the distance dn changes abruptly can be obtained.

[0121] Furthermore, as shown in FIG. 15(a), the labeling unit 23 generates an intersection plane CRP (in this embodiment, the intersection angle is set to 90° and it is a vertical plane) that passes through the boundary line BL and intersects the floor surface of the platform P which is the reference plane RP, and discriminates between the reference plane RP and the vertical plane which is the intersection plane CRP orthogonal to the reference plane RP, and labels each of them (SE15). Note that the size of the intersection plane CRP is not particularly limited and is arbitrary. Also, the intersection angle is not limited to 90°, and any angle can be used as long as foreign objects existing on the reference plane RP can be detected. For example, it can be set within a range of about 90 ± 5°.

[0122] Next, as shown in FIG. 15(b), the area setting unit 24 sets a width W that serves as the boundary in the width direction of the general detection area in the left - right direction along the boundary line BL with the center point CP on the boundary line BL where the distance from the lidar 10 is the shortest as a reference (SE16).

[0123] Next, the area setting unit 24 sets a depth D that is the boundary in the depth direction of the general-purpose detection area from the boundary line BL along the reference plane RP (SE17), and sets a height H that is the boundary in the height direction from the reference plane RP (SE18). As a result, a rectangular surface S1' with a width W and a height H is formed on the plane of the crossing plane CRP, and a rectangular surface S2' with a width W and a depth D is formed on the plane of the reference plane RP.

[0124] Next, the area setting unit 24 generates four auxiliary surfaces, namely auxiliary surfaces S3', S4' facing the rectangular surfaces S1', S2', and auxiliary surfaces S5', S6' perpendicular to the rectangular surfaces S1', S2' (SE19), and partitions a general-purpose detection area BA' surrounded by the six surfaces S1', S2', S3', S4', S5', S6' (SE20).

[0125] As shown in FIG. 15(c), the area setting unit 24 shifts the rectangular surface S1' corresponding to the crossing surface CRP in the depth direction by an offset amount ΔD for the general-purpose detection area BA' obtained in this manner. In addition, the rectangular surface S2' corresponding to the reference surface RP is shifted upward by an offset amount ΔH. The area setting unit 24 defines the rectangular parallelepiped region obtained in this manner as the final general-purpose detection area DA' (SE21). The area setting unit 24 stores the coordinates of each vertex indicating the general-purpose detection area DA' in the detection area information storage unit 30. The values ​​of the offset amounts ΔD and ΔH are not particularly limited, and may be set to values ​​different from those described in FIG. 11. Therefore, in this embodiment, the final general-purpose detection area DA' can be defined by acquiring the boundary line BL corresponding to the end of the platform P on the track surface side.

[0126] In the above embodiment, an example has been described in which the surface acquired by the surface acquisition unit 22 is a flat surface, but the present invention is not limited to flat surfaces, and may be applied to curved surfaces with small curvature. For example, a known curved surface fitting algorithm may be used, in which case the normal of the tangent plane to the point of interest may be adopted as the normal of the surface of interest.

[0127] In the above-described embodiment, an example has been described in which the detection area is an area defined between a safety fence SF provided on a platform P of a station building and a vehicle 1 parked on the platform P. However, the present invention is not limited to such an aspect, and can be applied to any facility that requires setting an appropriate detection area in an environment in which the detection area fluctuates.

[0128] For example, this can be applied to a case where a car that has entered and stopped in a multi-storey parking lot is used as a reference measurement object, and a detection area is set based on the coordinates of a distance measurement point including a reflection point on the surface of the car based on the reflected light of the measurement light irradiated to an area including the car before people get in or out of the car. When a transport mechanism that transports the car to a garage is operating, the presence of obstacles such as people and luggage around the car can be confirmed to ensure safety.

[0129] The point cloud data acquisition unit 21 is not limited to a light irradiating type as in the above embodiment, and may be any means capable of acquiring point cloud data, such as a passive stereo camera, an ultrasonic wave sensor, or a millimeter wave sensor.

[0130] The above-described embodiments are each an explanation of one example of the present invention, and the scope of the present invention should not be construed as being limited by the description. It goes without saying that the present invention can be appropriately modified and designed within the scope that achieves the effects of the present invention. [Explanation of symbols]

[0131] 1: Vehicle 2: Door 10: Rider 12: Signal processing circuit 20: Detection area setting section 21: Point cloud data acquisition unit 22: Surface acquisition part 23: Labeling Department 24: Area setting section 30: Detection area information storage unit 40: Obstacle detection unit 50: System control unit BA: Reference detection area DA: Detection area DR: Door SF: Safety fence

Claims

1. a point cloud data acquisition unit that acquires three-dimensional point cloud data indicating coordinates of distance measurement points including reflection points on a surface of a reference object based on reflected light of measurement light irradiated onto an area including the reference object to be set as a detection area; a surface acquisition unit that acquires a plurality of surfaces including a surface of the reference object based on the point cloud data acquired by the point cloud data acquisition unit; a labeling unit that labels the multiple faces acquired by the face acquisition unit under a predetermined condition; an area setting unit that sets the detection area for detecting an obstacle based on the plurality of faces labeled by the labeling unit; The present invention is configured to include The surface acquisition unit executes a process for acquiring, for a plurality of focus points, normals to a surface of interest generated at a focus point and a point adjacent to the focus point among a plurality of distance points included in the point cloud data, as normal vectors at the focus points, and executes a surface acquisition process for acquiring a plurality of surfaces including any of the focus points based on each normal vector.

2. 2. The detection area setting device of claim 1, wherein the surface acquisition process includes a clustering process in which, if the normal vectors of other points of interest existing within a specified search range from a specific point of interest are inclined within a specified allowable range with respect to the direction of the normal vector of the specific point of interest, the other points of interest are grouped together with the specific point of interest.

3. 3. The detection area setting device of claim 2, wherein the surface acquisition process includes a process of repeating the clustering process with each of the points of interest as the specific point of interest, and if any of the points of interest to be grouped in a subsequent clustering process have already been grouped in a previous clustering process, integrating all of the points of interest to be grouped in the subsequent clustering process into the same group as the previous clustering process.

4. 4. The detection area setting device according to claim 2 or 3, wherein the surface acquisition process includes an integration process in which a representative normal vector representing each of the multiple groups generated by the clustering process is obtained, and groups whose respective representative normal vectors fall within a predetermined tolerance range are integrated into the same group.

5. 5. The detection area setting device according to claim 2, wherein the surface acquisition process performs a filtering process on the points of interest grouped into the same group to remove outliers from the same group.

6. 6. The detection area setting device according to claim 5, wherein the filtering process is a process for performing robust estimation including any one of RANSAC, M-estimation, and least median method.

7. 7. The detection area setting device according to claim 2, wherein the surface acquisition process acquires a surface based on the attention points included in the same group.

8. The detection area setting device according to any one of claims 1 to 7, wherein the labeling unit identifies and labels a reference plane corresponding to the surface of the reference object from the multiple surfaces and an intersecting plane that intersects the reference plane based on a preset reference vector and normal vectors of the multiple surfaces acquired by the surface acquisition unit.

9. The detection area setting device of any one of claims 1 to 7, wherein the labeling unit identifies a reference surface corresponding to the surface of the reference object from the multiple surfaces based on a predetermined reference vector and the normal vectors of the multiple surfaces acquired by the surface acquisition unit, acquires a boundary line of the reference surface based on the point cloud data acquired by the point cloud data acquisition unit, generates an intersection plane that passes through the boundary line and intersects with the reference surface, and identifies and labels the reference surface and the intersection plane that intersects with the reference surface.

10. The detection area setting device according to claim 8 or 9, wherein the area setting unit defines a detection area consisting of a polyhedron between the reference surface and the irradiation source of the measurement light based on the reference surface and the intersection surface labeled by the labeling unit.

11. A detection area setting device according to any one of claims 1 to 10, a range sensor that scans a measurement light in an area including the detection area set by the detection area setting device and determines the presence or absence of an obstacle based on the presence or absence of reflected light from the detection area; An obstacle detection system comprising:

12. a point cloud data acquisition step of acquiring three-dimensional point cloud data indicating coordinates of distance measurement points including reflection points on a surface of a reference object based on reflected light of measurement light irradiated onto an area including the reference object to be set as a detection area; a surface acquiring step of acquiring a plurality of surfaces including a surface of the reference object based on the point cloud data acquired in the point cloud data acquiring step; a labeling step of labeling the plurality of faces acquired in the face acquisition step under a predetermined condition; an area setting step of setting the detection area for detecting an obstacle based on the plurality of faces labeled in the labeling step; The present invention is configured to include The surface acquisition step is a detection area setting method characterized in that a process of acquiring, for a plurality of attention points, normals to a surface of interest generated at a point of interest and a point near the point of interest among a plurality of distance points included in the point cloud data as normal vectors at the point of interest, and acquiring a plurality of surfaces constituted by any of the attention points based on each normal vector.

13. The detection area setting method according to claim 12, wherein the surface acquisition step includes executing a clustering process in which, if the normal vectors of other points of interest existing within a predetermined search range from a specific point of interest are inclined within a predetermined allowable range with respect to the direction of the normal vector of the specific point of interest, the other points of interest are grouped together with the specific point of interest.

14. The detection area setting method according to claim 13, wherein the surface acquisition step includes performing an integration process in which the clustering process is repeated with each of the attention points as the specific attention point, and if any of the attention points to be grouped in a later clustering process have already been grouped in a previous clustering process, all of the attention points to be grouped in the later clustering process are integrated into the same group as the previous clustering process.

15. 15. The detection area setting method according to claim 13, wherein the surface acquisition step includes executing an integration process in which a representative normal vector representing each of the plurality of groups generated by the clustering process is obtained, and groups whose respective representative normal vectors have orientations falling within the predetermined tolerance range are integrated into the same group.

16. 16. The detection area setting method according to claim 13, wherein the surface acquisition step performs a filtering process on the attention points grouped into the same group to remove outliers from the same group.

17. 17. The detection area setting method according to claim 16, wherein the filtering process is a process for performing robust estimation including any one of RANSAC, M-estimation, and least median method.

18. 18. A detection area setting method according to any one of claims 12 to 17, wherein the labeling step identifies and labels a reference plane corresponding to the surface of the reference object from the multiple surfaces and an intersecting plane that intersects the reference plane based on a preset reference vector and normal vectors of the multiple surfaces acquired in the surface acquisition step.

19. A detection area setting method according to any one of claims 12 to 17, wherein the labeling step identifies a reference surface corresponding to the surface of the reference object from the multiple surfaces based on a preset reference vector and normal vectors of the multiple surfaces acquired in the surface acquisition step, acquires a boundary line of the reference surface based on the point cloud data acquired in the point cloud data acquisition step, generates an intersection plane that passes through the boundary line and intersects the reference surface, and identifies and labels the reference surface and the intersection plane that intersects the reference surface.

20. 20. The detection area setting method according to claim 18 or 19, wherein the area setting step defines a detection area consisting of a polyhedron between the reference surface and the source of the measurement light based on the reference surface and the intersection surface labeled in the labeling step.

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