Control device, program, and LiDAR system

Abstract

A control device (20) for processing point group data from a LiDAR device (10) is provided with: a partition unit (22) for partitioning data indicating a space into a plurality of voxels (B); an imparting unit (23) that imparts an initial value f (0) of a variable to each voxel (B); a variation unit (24) that, on the basis of the point group data for each predetermined period, raises a variable f (n), which indicates that voxels (B) in which a predetermined number or more of points (r) at which the object is detected are present during a first period or more that is equal to or less than the predetermined period, by a predetermined rise range, and raises a variable f (n) of the other voxels by a predetermined fall range, said variable f (n) indicating that the voxels (B) are present during the first period or more that is equal to or less than the predetermined period; and an output unit (25) that, after the variation of the variable f (n) in the variation unit (24) is repeated a predetermined number of times, outputs data in which voxels (B) for which the variable f (n) is equal to or greater than a predetermined threshold (t) are distinguished from other voxels (B).

Description

Technical Field

[0001] This invention relates to control devices, programs, and LiDAR systems. Background Technology

[0002] LiDAR (Light Detection and Ranging) devices are known to be able to determine the distance and shape of an object by detecting the reflected light of an irradiated light. An object detection system using a LiDAR device is disclosed in Patent Document 1 below.

[0003] In the object detection system described in Patent Document 1 below, distance data of an object is obtained by a LiDAR device, the difference between the distance data and the pre-stored background data is calculated, and the location of the object is determined.

[0004] Patent Document 1: Japanese Patent Application Publication No. 2009-58927 Summary of the Invention

[0005] As described above, in the object detection system of Patent Document 1, background data is pre-stored. This background data is obtained by measuring the unoccupied monitoring area using a LiDAR device. However, when acquiring the background data, it may be impossible to acquire the background data if the moving object is located in the unoccupied monitoring area.

[0006] Therefore, the object of the present invention is to provide a control device, program, and LiDAR system that can distinguish data that can be used as background data from other data, even when a moving object is in the monitored area.

[0007] To achieve the above objectives, the present invention provides a control device for processing point cluster data output from a LiDAR device, characterized by comprising: a separation unit that divides spatial data into multiple voxels; an assignment unit that assigns initial values ​​of variables to each voxel; a variation unit that, for each predetermined period, based on the point cluster data input from the LiDAR device, performs one of the following variations on the voxels: for voxels indicating that a predetermined number of points where objects are detected are present in a first period or longer than the predetermined period, a variation that increases by a predetermined rate and decreases by a predetermined rate; and for voxels indicating that fewer points where objects are detected in a first period or longer than the predetermined number, a variation that increases by a predetermined rate and decreases by a predetermined rate; and an output unit that, after repeating the variation of the variables in the variation unit a predetermined number of times, outputs data that distinguishes voxels whose variables are above a predetermined threshold from other voxels.

[0008] Furthermore, the present invention is a program executed by a control device that processes point cluster data output from a LiDAR device, characterized in that the control device performs the following steps: dividing the data representing the space into multiple voxels; assigning initial values ​​to variables for each voxel; for each specified period, based on the point cluster data input from the LiDAR device, performing either a predetermined increase or a predetermined decrease on the variable representing voxels where a predetermined number of points indicating the detection of objects exists in a first period below the specified period, and performing either a predetermined increase or a predetermined decrease on the variable representing voxels where a predetermined number of points indicating the detection of objects exists in a first period above the specified period; repeating the step of performing the variable changes a predetermined number of times, and outputting data that distinguishes voxels whose variables are above a predetermined threshold from other voxels.

[0009] Furthermore, the LiDAR system of the present invention includes a LiDAR device and a control device that inputs point cluster data from the LiDAR device. The control device is characterized by having: a partitioning unit that partitions spatial data into multiple voxels; an assignment unit that assigns initial values ​​of variables to each voxel; a variation unit that, for each predetermined period, based on the point cluster data input from the LiDAR device, performs one of the following variations on the voxels: for voxels indicating that a predetermined number of points where objects are detected exists during a first period less than the predetermined period, a variation that increases by a predetermined rate and decreases by a predetermined rate; and for voxels indicating that fewer points where objects are detected during the first period than the predetermined number, a variation that increases by a predetermined rate and decreases by a predetermined rate; and an output unit that, after repeating the variation of the variables in the variation unit a predetermined number of times, outputs data that distinguishes voxels whose variables are above a predetermined threshold from other voxels.

[0010] Based on the aforementioned control device, program, and LiDAR system, the following first or second action is performed. In the first action, for voxels where a predetermined number of points detecting objects exist during a first period, the variable increases for each predetermined period; for voxels where the number of points is less than the predetermined number, the variable decreases for each predetermined period. Furthermore, in locations that become background, detected objects such as walls do not move. Therefore, in voxels representing areas where background objects such as walls are located, the variable continuously increases. Additionally, in voxels representing areas where moving objects are located, even if the variable increases during the temporary period when the moving object is present, the variable decreases before the moving object is located in that area or after the moving object has moved from that area. Therefore, in voxels representing areas that may become background, the variable may become above a predetermined threshold after repeating the change a predetermined number of times; in voxels representing other areas, the variable may become below the predetermined threshold after repeating the change a predetermined number of times.

[0011] Furthermore, in the second operation, according to the aforementioned control device, program, and LiDAR system, in voxels where a predetermined number of points detecting objects exist during the first period, the variable decreases for each predetermined period; in voxels where the number of points is less than the predetermined number, the variable increases for each predetermined period. Additionally, in voxels representing areas where objects such as walls become background, the variable continuously decreases. Furthermore, in voxels representing areas where moving objects are located, even if the variable decreases during the temporary period when the moving object is present, the variable increases before the moving object is in that area or after the moving object has moved from that area. Therefore, in voxels representing areas that may become background, the variable may fall below a predetermined threshold after repeating the change a predetermined number of times; in voxels representing other areas, the variable may become above the predetermined threshold after repeating the change a predetermined number of times.

[0012] Therefore, in either the first or second action, data is output that distinguishes voxels whose variables are above a predetermined threshold from other voxels, thereby enabling the differentiation of voxels that may serve as background data from other voxels. Thus, according to the present invention, even when a moving object is located within a monitored area, data that can be used as background data can be distinguished from other data. Therefore, a background can be set in the device receiving this data.

[0013] Alternatively, the initial value may be determined, within a specific period, for each of the voxels, based on the point cluster data, according to the number of points indicating that an object was detected in a second period or more below the specific period. Or, the initial value may be determined, within a specific period, for each of the voxels, based on the point cluster data, according to the number of points indicating that an object was detected in a second period or more below the specific period and the duration of that point.

[0014] When initial values ​​are determined in this way, in the first operation, for the variables used as initial values, the variables representing voxels that may become background can be set to higher values, and the variables of other voxels can be set to lower values. Furthermore, in the second operation, for the variables used as initial values, the variables representing voxels that may become background can be set to lower values, and the variables of other voxels can be set to higher values. That is, the initial values ​​of each voxel can be weighted. Therefore, the period during which the variables representing voxels that become background are above a predetermined threshold can be shortened.

[0015] Alternatively, the length of the specific period may be the same as the length of the specified period. In this case, the length of the second period may be the same as the length of the first period.

[0016] In these situations, management during the period can be easily carried out.

[0017] It is possible that the initial value is the same in each of the voxels.

[0018] In this case, the initial value can be set to a specific value, thus making the process of determining the initial value easier and simplifying control.

[0019] Preferably, when the initial value is the same in each of the voxels, the initial value is the specified threshold.

[0020] In this case, even if the variable changes only once, the variable can be above a predetermined threshold in voxels representing regions that can become background, and the variable can be below a predetermined threshold in voxels representing regions that do not become background. Therefore, the time required to output data from the output unit that distinguishes between voxels where the variable is above the predetermined threshold and other voxels can be shortened.

[0021] It is possible that the specified quantity is 1.

[0022] In this case, it is possible to distinguish between voxels representing regions that may become background and the other voxels mentioned above by representing points that detect small objects.

[0023] It is permissible if the specified quantity is 2 or more.

[0024] In this case, by introducing noise into a specific location, it is possible to suppress the possibility that voxels representing the region containing that location will become background.

[0025] It is possible that at least one of the specified increase range and the specified decrease range is a constant value.

[0026] In this case, for example, the processing can be simplified compared to situations where the increase or decrease varies for each specified period.

[0027] Additionally, preferably, at least one of the specified rise and the specified fall is determined for each of the voxels based on the number of points indicating that an object was detected for more than the first period during the specified period. Alternatively, preferably, at least one of the specified rise and the specified fall is determined for each of the voxels based on the number of points indicating that an object was detected for more than the first period during the specified period and the duration of that point.

[0028] When determining the magnitude of increase and decrease as described above, the difference in variation between the voxels representing regions that could become background and other voxels can be increased. Therefore, it is possible to more appropriately distinguish between voxels representing regions that could become background and other voxels.

[0029] In addition, preferably, the length of the first period is the same as the length of the specified period.

[0030] Objects that become background objects generally do not move. Therefore, the points used to detect these objects always exist within a voxel. Consequently, points detected temporarily during a specified period can be excluded as noise. Thus, voxels representing regions that may become background objects can be more accurately distinguished from other voxels.

[0031] As described above, according to the present invention, a control device, a program, and a LiDAR system are provided that can distinguish data that can be used as background data from other data even when the moving body is in the monitored area. Attached Figure Description

[0032] Figure 1 This is a schematic diagram illustrating a LiDAR system according to an embodiment of the present invention.

[0033] Figure 2 This is a conceptual diagram representing the monitored area.

[0034] Figure 3 It is a flowchart showing the operation of the control device. Detailed Implementation

[0035] Hereinafter, preferred embodiments of the control device, program, and LiDAR system of the present invention will be described in detail with reference to the accompanying drawings. The following illustrative embodiments are provided for ease of understanding and are not intended to limit or explain the invention. Within the scope of the claims, modifications and alterations can be made without departing from the spirit of the invention. Furthermore, the components of the following illustrative embodiments can be appropriately combined. It should be noted that in the following accompanying drawings, the dimensions of various components are sometimes changed for ease of understanding. Additionally, in the drawings, for ease of observation, sometimes only a portion of the same components is labeled with reference numerals, while some reference numerals are omitted.

[0036] Figure 1 This is a schematic diagram illustrating the LiDAR system of this embodiment. The LiDAR system 1 of this embodiment includes a LiDAR device 10, a control device 20, a memory 30, and a monitor 40 as its main components.

[0037] The LiDAR device 10 of this embodiment detects objects within its monitoring area. Through this detection, the LiDAR device 10 can detect objects in the background such as walls and benches, as well as moving objects such as people and vehicles. The LiDAR device 10 of this embodiment is, for example, a LiDAR device using a raster scanning method. The LiDAR device 10 of this embodiment includes a housing 19, a drive circuit 11, a laser source 12, a drive mirror 13 for H-direction scanning, a drive mirror 14 for V-direction scanning, a light-receiving element 15, and a point group data generation unit 16. It should be noted that... Figure 1 In the example, LiDAR device 10 is a mechanical LiDAR device, but it can also be a phased array LiDAR device without a drive unit.

[0038] The cover 19 has a storage space for the drive circuit 11, the laser source 12, the drive mirror 13 for scanning in the H direction, the drive mirror 14 for scanning in the V direction, the light receiving element 15, and the dot group data generation unit 16, so that the laser Lb emitted from the laser source 12 and the reflected light Lr reflected by the laser Lb by the object in the monitored area can pass through.

[0039] The driving circuit 11 is composed of, for example, multiple logic circuits, and is electrically connected to the laser source 12, the H-direction scanning driving mirror 13, and the V-direction scanning driving mirror 14 to control them.

[0040] Laser source 12 emits laser light Lb of a predetermined wavelength. This laser Lb is, for example, near-infrared light with a wavelength of 905 nm or 1550 nm. The timing of laser light Lb emission from laser source 12 is controlled by drive circuit 11, which emits laser Lb based on signals from drive circuit 11. It should be noted that drive circuit 11 is electrically connected to point group data generation unit 16, and outputs data including the timing of laser light Lb emission from laser source 12 to point group data generation unit 16.

[0041] The H-direction scanning drive mirror 13 includes a reflector that reflects the laser Lb emitted from the laser source 12 and a drive unit (not shown) controlled by the drive circuit 11. When reflecting the laser Lb, the H-direction scanning drive mirror 13 reflects the laser Lb while changing the reflection angle in the horizontal direction via the drive unit. By changing the reflection angle of the H-direction scanning drive mirror 13, the LiDAR device 10 performs a horizontal scan.

[0042] The V-direction scanning drive mirror 14 includes a reflector that reflects the laser Lb reflected by the H-direction scanning drive mirror 13 and a drive unit (not shown) controlled by the drive circuit 11. When reflecting the laser Lb, the V-direction scanning drive mirror 14 reflects the laser Lb while changing the reflection angle in the vertical direction via the drive unit. This change in the reflection angle of the V-direction scanning drive mirror 14 changes the position of the horizontal scan performed by the LiDAR device 10 in the vertical direction. The laser reflected by the V-direction scanning drive mirror 14 passes through the cover 19 and illuminates the front of the vehicle VE.

[0043] The H-direction scanning drive mirror 13 and the V-direction scanning drive mirror 14 can be configured to include a multifaceted mirror or a galvanometer. Alternatively, the H-direction scanning drive mirror 13 and the V-direction scanning drive mirror 14 can each be constructed from MEMS mirrors. Furthermore, the H-direction scanning drive mirror 13 and the V-direction scanning drive mirror 14 can be combined into one using a dual-axis scanning type reflector, and the order in which the laser Lb is reflected from the H-direction scanning drive mirror 13 and the V-direction scanning drive mirror 14 can be interchanged.

[0044] The light-receiving element 15 is an element that receives the reflected light Lr after the laser Lb is reflected by an object in the monitored area. The reflected light Lr received by the light-receiving element 15 contains information about the object located within the monitored area. The light-receiving element 15 is electrically connected to the dot group data generation unit 16, and this information is input to the dot group data generation unit 16 as an electrical signal.

[0045] The point group data generation unit 16 generates point data for each reflection position based on data related to the emission timing of the laser Lb input from the drive circuit 11, information input from the light-receiving element 15, and data on the timing of the information input from the light-receiving element 15, and based on the direction of the reflection position of the laser Lb and the distance to that reflection position. The point data includes the coordinates of the point. Therefore, the point group data generation unit 16 generates point group data as a collection of point data. The point group data generation unit 16 is electrically connected to the control device 20, and the point group data is input to the control device 20.

[0046] Next, the control device 20 will be described.

[0047] The control device 20 may be composed of, for example, integrated circuits such as microcontrollers, ICs (Integrated Circuits), LSIs (Large-scale Integrated Circuits), ASICs (Application Specific Integrated Circuits), or NC (Numerical Control) devices. Furthermore, the control device 20 may or may not use a machine learner.

[0048] The control device 20 has an input interface 21, a partition 22, an imparting part 23, a variable part 24, an output part 25, and an output interface 29, which are electrically connected via a bus.

[0049] Input interface 21 is electrically connected to point group data generation unit 16. Therefore, point group data is input from input interface 21.

[0050] The partition 22 divides the data represented by the three-axis coordinates of the space into multiple voxels. This space is the space monitored by the LiDAR system 1. Figure 2 This is a conceptual diagram representing the monitored area. Figure 2 In the diagram, voxel B is represented by a dashed line. It should be noted that, to avoid complicating the diagram, Figure 2 Represented as a planar diagram, but actually divided into voxels B along the x, y, and z directions according to each defined interval. Furthermore, although in Figure 2 The text is presented in a simplified manner, but the size of voxel B is greater than... Figure 2 The size shown is small, and the AR monitoring area is divided into smaller segments than... Figure 2 The voxels shown are those with more voxels B. Therefore, the coordinates of each voxel B are calculated as data, and the calculated data is sent to the variable unit 24 via the bus.

[0051] It should be noted that each voxel B is set to a size capable of containing multiple points r representing multiple reflection points. That is, for example, when there are reflection positions adjacent to each other in the H direction, and the reflected light Lr reflected at each reflection position forms data for multiple points r, each point r can be located in one voxel B. Figure 2 In the example, the stationary object F and the moving body M are located in regions represented by voxels B1-B3 and B4-B6, respectively. Within these regions, represented by voxels B2, B3, and B4-B6, there are multiple reflection points of the laser Lb reflected by the stationary object F and the moving body M. Therefore, multiple detection points r are located in voxels B2, B3, and B4-B6. However, depending on the position of the stationary object F, there are also regions, such as the region represented by voxel B1, where the reflection point is only one point. In this case, voxel B1 has only one point r.

[0052] The assignment unit 23 assigns an initial value f(0) to the variable f(n) for each voxel B. In this embodiment, the initial value f(0) is the same for each voxel B. By making the initial value f(0) consistent for each voxel B, the initial value f(0) can be made to be a specific value, making the process of determining the initial value f(0) easier, simplifying control, and reducing the load on the assignment unit 23.

[0053] The variation unit 24 varies the variable f(n) for each specified period. If it is the first variation of the variable after the initial value f(0) has just been set, the initial value f(0) is varied. Specifically, the variation unit 24, based on the point group data input from the LiDAR device 10, increases the variable f(n) by a specified increase rate, indicating that there are more than a specified number of voxels B at points r that are detected in the first period below the specified period. Conversely, the variation unit 24 decreases the variable f(n) by a specified decrease rate, indicating that there are fewer than a specified number of voxels B at points r that are detected in the first period above the specified period.

[0054] The specified quantity is, for example, 1. In this case, it is possible to distinguish between voxels B representing regions that could become background due to small objects being located within the area shown by voxel B and the detection point r of such objects being only one, and the other voxels B mentioned above. It should be noted that the specified quantity can also be 2 or more. In this case, by allowing noise to enter a specific location, it is possible to suppress the possibility of voxels B representing regions containing that location becoming background. In addition, the specified period is, for example, the period during which 12 frames of point group data are input from the LiDAR device 10. It should be noted that the number of frames is not limited to 12 frames. For example, it can also be a specified number of frames of 4 or more and 36 or less. In addition, the first period is, for example, the period during which 10 frames of point group data of 12 or less are input from the LiDAR device 10. It should be noted that the number of frames representing the first period is not limited to 10 frames. For example, if the first period is less than or equal to the specified period, it can also be a specified number of frames of 4 or more and 24 or less. In this case, when there are more than 10 consecutive frames within a specified period indicating that there are more than 10 points r of objects detected at the specified coordinates, the variable f(n) of the voxel B at that coordinate increases by a specified increase. When there are more than 10 consecutive frames within a specified period indicating that there are fewer than 10 points r of objects detected at the specified coordinates, the variable f(n) of the voxel B at that coordinate decreases by a specified decrease.

[0055] The specified increase and decrease ranges can also be equal. Alternatively, the specified increase and decrease ranges are preferably constant values. In this case, for example, processing can be simplified compared to cases where the increase and decrease ranges vary for each specified period. When the specified increase and decrease ranges are constant values ​​'a', and the changed variable is set to f(n+1), the variable changes in the manner of f(n+1) = f(n) + a and f(n+1) = f(n) - a. It should be noted that, with the changed variable set in this way, counter control is performed so that after the change, n+1 becomes n. Details of this control will be described later.

[0056] After repeating the variation of variable f(n) in variation unit 24 a predetermined number of times, output unit 25 outputs data that distinguishes voxels B whose variable f(n) is above a predetermined threshold t from other voxels B. Therefore, output unit 25 compares the predetermined threshold t with the variable f(n) of each voxel B, for example, assigning a predetermined flag to voxels B whose variable f(n) is above the threshold t, and assigning other flags to voxels B whose variable f(n) is below the threshold t. Alternatively, it can perform processing that assigns a flag only to voxels B whose variable f(n) is above the threshold t and voxels B whose variable f(n) is below the threshold t.

[0057] The specified number of times is not particularly limited, for example, as long as it is 1 or more. If the specified number is 1, the output interval from the output unit 25 becomes shorter, and the data representing voxels B that may become background can be updated in a short period of time according to changes in the situation. In addition, if the specified number is a larger value, voxels B that may become background can be determined more reliably. This larger value is, for example, 50 or more and 200 or less, 80 or more and 120 or less, or 100.

[0058] Furthermore, the threshold t is preferably equal to the initial value f(0). By making the initial value f(0) equal to the threshold t, even if the variable f(n) changes only once, in voxels B representing regions that may become background, the variable f(n) may be above the threshold t, while in voxels B representing other regions, the variable f(n) may be below the threshold t. Therefore, the period before outputting data from the output unit 25 that distinguishes between voxels B where the variable f(n) is above the threshold t and other voxels B can be shortened. However, the threshold t and the initial value f(0) can also be different.

[0059] The control device 20 is electrically connected to the memory 30. It should be noted that... Figure 1 The terminals between the memory 30 and the control device 20 are omitted in the original text. The memory 30 is configured to store and retrieve information. The memory 30 is, for example, a non-transitory recording medium, preferably a semiconductor recording medium such as RAM (Random Access Memory) or ROM (Read Only Memory), but can include any form of recording medium such as optical recording media or magnetic recording media. It should be noted that "non-transitory" recording media includes all computer-readable recording media except for transient propagating signals, and does not exclude volatile recording media. It should be noted that the memory 30 and the control device 20 can also be housed in an integrated package. The memory 30 stores various programs for controlling the control device 20 or generating information, as well as data required for generating information. The control device 20 reads the programs and information stored in the memory 30. In addition, the memory 30 stores information according to instructions from the control device 20.

[0060] Monitor 40, for example, displays the status of each voxel B as shown by control device 20. Monitor 40 displays, for example, a voxel B whose color differs from other voxels B when the variable f(n) exceeds a threshold t. It should be noted that monitor 40 is not a necessary component of LiDAR system 1.

[0061] Next, the operation of the control device 20 will be explained.

[0062] Figure 3 This is a flowchart illustrating the actions of the control device 20. The program for executing the actions shown in the flowchart is stored in the memory 30. Therefore, the control device 20 executes the actions by reading the program from the memory 30. Figure 3 The flowchart. For example... Figure 3 As shown, the operation of the control device 20 in this embodiment includes steps S1 to S7.

[0063] <Step S1>

[0064] This step is a partitioning step that divides the data in the representation space into multiple voxels B. In this step, as... Figure 2 As shown, the partition 22 of the control device 20 divides the data representing the space shown in the 3-axis coordinate system into multiple voxels B. As explained above, the partition 22 calculates the coordinates of each voxel B as data, and the calculated data is sent to the variation unit 24 via the bus.

[0065] <Step S2>

[0066] This step sets the counter's count, n, to an initial value of 0. Therefore, after this step, n = 0.

[0067] <Step S3>

[0068] This step is to assign an initial value f(0) to the variable f(n) for each voxel B. As mentioned above, the variable f(n) is n = 0, so the variable f(n) after this step is f(0). In this embodiment, the initial value f(0) is the same in each voxel B as described in the assignment section 23, for example, the same as the threshold t mentioned above.

[0069] <Step S4>

[0070] This step is a variation step that changes the variable f(n) for each specified period. In this step, as explained above in variation section 24, variation section 24, based on the point cluster data input from LiDAR device 10, increases the variable f(n) by a specified increase rate, indicating that there are more than a specified number of voxels B at points r that detect objects in the first period below the specified period. Conversely, variation section 24 decreases the variable f(n) by a specified decrease rate, indicating that there are fewer than a specified number of voxels B at points r that detect objects in the first period above the specified period. The specified number, increase rate, and decrease rate are as explained above in variation section 24.

[0071] It should be noted that each voxel B is set to a size capable of containing multiple points r representing multiple reflection points. That is, for example, when there are reflection positions adjacent to each other in the H direction and the data of point r is formed by the reflected light Lr reflected at each reflection position, each point r can be located in one voxel B. Figure 2 In the example, the stationary object F and the moving object M are located in regions represented by voxels B1-B3 and B4-B6, respectively. Multiple reflection positions of the reflected laser Lb exist in the regions represented by voxels B2, B3, and B4-B6, while only one reflection position of the reflected laser Lb exists in the region represented by voxel B1. Therefore, multiple points r for detecting the object exist in voxels B2, B3, and B4-B6, while only one point r for detecting the object exists in voxel B1.

[0072] Voxels B1 to B3 represent the region where the fixed object F is located. Therefore, among voxels B1 to B3, the point r representing the reflection point exists for more than the aforementioned first period. Thus, for example, when the specified quantity is 1 as described above, the variable unit 24 increases the variable f(n) of voxels B1 to B3 by a specified increase. On the other hand, for example, when the specified quantity is 2, the variable unit 24 increases the variable f(n) of voxels B2 and B3 by a specified increase, but decreases the variable f(n) of voxel B1, where the number of points r is 1, by a specified decrease.

[0073] Voxels B4 to B6 represent the region where the moving body M is located. Therefore, among voxels B4 to B6, there are cases where the point r representing the reflection point exists for more than the first period, and cases where it does not exist for more than the first period. In any of voxels B4 to B6, if the number of points r that exist for more than the first period is more than a predetermined number, the variable unit 24 increases the variable f(n) of voxel B by a predetermined increase. Conversely, in any of voxels B4 to B6, if the number of points r that exist for more than the first period is less than a predetermined number, the variable unit 24 decreases the variable f(n) of voxel B by a predetermined decrease.

[0074] As explained above in section 24, when the specified increase and decrease are constant values ​​a, and the variable after this step is set to f(n+1), the variable becomes f(n+1) = f(n) + a or f(n+1) = f(n) - a. Therefore, the variable of voxel B representing the region where the object is located increases, and the variable of voxel B representing the region where the object is not located decreases.

[0075] <Step S5>

[0076] This step advances the counter. n = n + 1 means adding 1 to the value of n before this step to obtain the new n. Therefore, the n on the right side of the equation represents the n before this step, and the n on the left side represents the n after this step. After the first step S4, n = 0; therefore, after this step following the first step S4, n = 1.

[0077] <Step S6>

[0078] This step is used to differentiate the next step based on whether step S4 has been performed a predetermined number of times. In this step, n is equal to the number of times step S4 has been performed. Therefore, if the predetermined number is set to X, then if n < X, the control device 20 returns to step S4; if n ≥ X, it proceeds to step S7.

[0079] <Step S7>

[0080] This step is an output step that distinguishes voxels B whose variable f(n) is above a predetermined threshold t from other voxels B. In this step, the output unit 25 compares the predetermined threshold t with the variable f(n) of each voxel B. Then, the output unit 25 generates data that assigns a predetermined flag to voxels B whose variable f(n) is above the threshold t. In addition, the output unit 25 generates data that assigns other flags to voxels B whose variable f(n) is less than the threshold t. Then, the output unit 25 outputs the data of the voxels B that have been assigned the flags. Thus, in this embodiment, the output distinguishes voxels B whose variable f(n) is above the predetermined threshold t from other voxels B by flags. This output is output from the output interface 29 via the bus.

[0081] As described above, voxels B1 to B3 represent the regions where the fixed object F is located. Therefore, among voxels B1 to B3, for voxel B where the variable f(n) increases by a predetermined increase rate in step S4 as described above, whenever step S4 is repeated, the variable f(n) increases, and in this step, the variable f(n) is highly likely to be above the threshold t.

[0082] Furthermore, as mentioned above, voxels B4 to B6 represent the regions where the moving body M is located. Therefore, among voxels B4 to B6, even if the variable f(n) increases by a predetermined increase in step S4 due to the change unit 24, there is also a possibility that the variable f(n) decreases by a predetermined decrease in the next step S4. Therefore, if the moving body M temporarily stops, the variable f(n) may become above the threshold t in this step; if the moving body M moves, the variable f(n) may become below the threshold t in this step.

[0083] In this embodiment, in this step, the output data is input to the monitor 40, and the monitor 40 displays voxels B that have been given a specified mark and voxels B that have been given other marks in a visually distinguishable manner.

[0084] After this step, the control device 20 returns to step S2 and repeats the above steps S2 to S7.

[0085] As explained above, based on the control device, program, and LiDAR system, even when the moving body is located in the monitoring area of ​​this embodiment, data that can be used as background data can be distinguished from other data.

[0086] Next, variations of the above-described embodiments will be described.

[0087] (Variation Example 1)

[0088] In the above embodiment, the initial value f(0) assigned by the assignment unit 23 to each voxel B is the same value in each voxel B. However, the initial value f(0) may not be the same value in each voxel B. In this modified example, the assignment unit 23 determines the initial value f(0) for each voxel B during a specific period, based on the dot group data and the number of points r indicating that an object was detected in the second period or more below the specific period. That is, when determining the initial value f(0) in each voxel B, in step S3, the assignment unit 23 counts the number of points r indicating that an object was detected in each voxel B in the second period or more during the specific period. And, the more points r there are, the larger the initial value f(0) is set to. This specific period is, for example, the period during which 12 frames of dot group data are input from the LiDAR device 10. For example, the specific period is the same length as the specified period in step S4 of the above embodiment. In this case, the second period is, for example, 10 frames less than 12 frames. If the specific period is the same length as the specified period in step S4 of the above embodiment, the first period in step S4 and the second period in step S2 can also be the same length. However, the specific period can be the same length as the specified period, and the first period and the second period can be different lengths. Alternatively, the specific period can be different lengths from the specified period.

[0089] (Variation Example 2)

[0090] This variation, like Variation 1, is an example where the initial value f(0) is not the same in each voxel B. In this variation, the assignment unit 23 determines the initial value f(0) for each voxel B within a specific period, based on the point group data, according to the number of points r indicating that an object was detected in the second period or more below the specific period and the duration of the point r. That is, for each voxel B, the initial value f(0) is determined based on the occupancy of the point r. When determining the initial value f(0) in each voxel B, in step S3, the assignment unit 23 counts the number of points r indicating that an object was detected in each voxel B in the second period or more in the specific period, just as in Variation 1, and then counts the duration of each point r. This duration is counted, for example, based on the number of frames. Furthermore, the more points r there are and the longer the duration of the point r, the larger the initial value f(0) is set to. The specific period and the second period in this variation are the same as in Variation 1.

[0091] When the initial value f(0) is determined as in Variations 1 and 2, the initial value f(0) of the variable representing the voxel B that may become background can be set to a higher value, and the initial value f(0) of the variable representing other voxel B can be set to a lower value, thus weighting the initial values ​​of each voxel B. Therefore, the period during which the variable representing the voxel B that becomes background becomes above a predetermined threshold t can be shortened. It should be noted that, in the cases of Variations 1 and 2, the second period is preferably the same length as the specific period. The object that becomes background does not move in principle. Therefore, the point that detects the object always exists within voxel B. Therefore, by making the second period the same length as the specific period, the temporarily detected point r in the specific period can be excluded as noise. Therefore, a clear difference can be assigned to the initial value f(0) of the voxel B that may become background and the initial value f(0) of other voxel B.

[0092] (Variation Example 3)

[0093] In the above embodiment, the variation unit 24 makes the increase and decrease of the variable f(n) of each voxel B a constant value a. However, the increase and decrease can also be different constant values. For example, the increase can be a constant value a, and the decrease can be a constant value b that is larger than a. In this case, the variable f(n) of voxel B representing the region where the moving body is located increases, but after the moving body moves from that region, the variable f(n) of that voxel B can decrease earlier. Alternatively, the increase can be a constant value a, and the decrease can be a constant value b that is smaller than a.

[0094] (Variation Example 4)

[0095] The magnitude of the increase or decrease in the variable f(n) of each voxel B caused by the variable unit 24 can also differ for each voxel B. In this modified example, at least one of the predetermined magnitude of increase and the predetermined magnitude of decrease is determined for each voxel B based on the number of points r indicating that an object was detected for more than one period within a predetermined period. That is, when determining the magnitude of increase or decrease in each voxel B, in step S4, the variable unit 24 counts the number of points r indicating that an object was detected for more than one period in each voxel B within a predetermined period. Furthermore, when the number of points r is greater than or equal to a predetermined number, the more points r there are, the larger the magnitude of increase set by the variable unit 24. Conversely, when the number of points r is less than the predetermined number, the fewer points r there are, the larger the magnitude of decrease set by the variable unit 24. However, when the number of points r is less than the predetermined number and the variable unit 24 sets the magnitude of decrease to a larger value, the predetermined number is 2 or more.

[0096] (Variation Example 5)

[0097] Similar to Variation 4, in this variation, the variation unit 24 causes the increase and decrease of the variable f(n) of each voxel B to differ in each voxel B. In this variation, at least one of the predetermined increase and decrease is determined for each voxel B based on the number of points r indicating that an object was detected for more than a first period during the predetermined period and the duration of the existence of those points r. That is, for each voxel B, the initial value f(0) is determined based on the occupancy of points r. When determining the increase or decrease in each voxel B, in step S4, the variation unit 24 counts the number of points r indicating that an object was detected for more than a first period in each voxel B during the predetermined period, similar to Variation 3, and then counts the duration of existence of each point r. This duration is counted, for example, based on the number of frames. Furthermore, when the number of points r is greater than or equal to a predetermined number, the more points r there are and the longer the duration of the points r, the larger the increase value is set by the variation unit 24. Furthermore, when the number of points r is less than the specified number, the fewer the number of points r and the shorter the duration of points r, the larger the decrease will be set by the variable unit 24. In this modified example, when the variable unit 24 sets the decrease to a larger value because the fewer the number of points r and the shorter the duration of points r, the specified number is 2 or more.

[0098] When the initial value f(0) is determined as in Variations 1 and 2, or when the magnitude of increase or decrease is determined as in Variations 4 and 5, the difference in variation of the variable can be increased between the variable representing the voxel B that may become the background region and other voxels B. Therefore, it is possible to more appropriately distinguish between the voxel B representing the voxel B that may become the background region and other voxels B. It should be noted that, as in Variations 4 and 5, one of the magnitude of increase or decrease can be determined, and the other can be set to a constant value a.

[0099] Furthermore, the first period is preferably the same length as the specified period. Objects that become background objects generally do not move. Therefore, the point detecting this object always exists within voxel B. Thus, by making the first period the same length as the specified period, temporarily detected points r within the specified period can be excluded as noise. Therefore, voxels B representing regions that may become background objects can be more accurately distinguished from other voxels B.

[0100] As explained above, one aspect of the present invention in the above embodiments and variations is a control device 20 that processes point group data output from a LiDAR device 10, comprising: a partitioning unit 22 that partitions spatial data into multiple voxels B; an assignment unit 23 that assigns an initial value f(0) to each voxel B; a variation unit 24 that, for each predetermined period, varies a variable f(n) based on point group data input from the LiDAR device 10 such that the variable f(n) representing the presence of a predetermined number or more voxels B in a first period or longer than the predetermined period increases by a predetermined increase, and the variable f(n) representing the presence of fewer voxels B in a first period or longer than the predetermined number of voxels B in the first period or longer decreases by a predetermined decrease; and an output unit 25 that, after repeating the variation of variable f(n) in the variation unit 24 a predetermined number of times, outputs data that distinguishes voxels B in which the variable f(n) is at or above a predetermined threshold t from other voxels B.

[0101] In addition, other aspects of the present invention in the above-described embodiments and variations are performed by a program executed by a control device 20 that processes point group data output from the LiDAR device 10. The control device 20 performs the following steps: dividing the data representing the space into multiple voxels B; assigning an initial value f(0) to each voxel B of variable f(n); for each specified period, based on the point group data input from the LiDAR device 10, changing the variable f(n) such that the variable f(n) representing the existence of a specified number or more voxels B where an object is detected in a first period less than the specified period increases by a specified increase, and the variable f(n) representing the existence of fewer voxels B where an object is detected in a first period less than the specified period decreases by a specified decrease; after repeating the step of changing the variable f(n) a specified number of times, outputting data that distinguishes voxels B where the variable f(n) is a specified threshold t or higher from other voxels B.

[0102] Another aspect of the present invention, including the above-described embodiments and variations, is a LiDAR system 1, which includes a LiDAR device 10 and a control device 20 that inputs point group data from the LiDAR device 10. The control device 20 includes: a partitioning unit 22 that partitions spatial data into multiple voxels B; an assignment unit 23 that assigns an initial value f(0) to each voxel B; a variation unit 24 that, for each specified period, varies a variable f(n) based on the point group data input from the LiDAR device 10 such that the variable f(n) representing the presence of a specified number or more voxels B at a first period less than the specified period increases by a specified increase, and the variable f(n) representing the presence of fewer voxels B at a first period less than the specified number decreases by a specified decrease; and an output unit 25 that, after repeating the variation of variable f(n) in the variation unit 24 a specified number of times, outputs data that distinguishes voxels B at a specified threshold t from other voxels B.

[0103] According to the control device 20, the program, and the LiDAR system 1, when there are more than a predetermined number of voxels B at a point r where an object is detected for a first period, the variable f(n) increases for each predetermined period; when there are fewer than a predetermined number of voxels B at a point r, the variable f(n) decreases for each predetermined period. At locations that become background, detected objects such as walls do not move. Therefore, in voxels B representing areas where background objects such as walls are located, the variable f(n) continuously increases. Furthermore, in voxels B representing areas where moving objects are located, even if the variable f(n) increases during a temporary period when the moving object is present, the variable f(n) decreases before the moving object is located in that area or after the moving object has moved from that area. Therefore, in voxels B representing areas that may become background, after repeating the change of the variable f(n) a predetermined number of times, the variable f(n) may become above a predetermined threshold t; in voxels B representing other areas, after repeating the change of the variable f(n) a predetermined number of times, the variable f(n) may be below the predetermined threshold t. Therefore, by outputting data that distinguishes between voxel Bs where the variable f(n) is above a predetermined threshold t and other voxel Bs, it is possible to distinguish between voxel Bs that may become background data and other voxel Bs. Thus, according to the present invention, even when a moving object is located within a monitored area, data that can be used as background data can be distinguished from other data. Therefore, a background can be set in the device receiving this data.

[0104] Here, another variation of the invention will be described.

[0105] (Variation Example 6)

[0106] In the above-described embodiments and modified examples, the modification unit 24 performs the following variable modification: for each specified period, based on the point cluster data input from the LiDAR device 10, the variable f(n) indicating that there are more than a specified number of voxels B in the first period or more below the specified period increases by a specified increase, and the variable f(n) indicating that there are fewer than a specified number of voxels B in the first period or more below the specified period decreases by a specified decrease. However, the modification unit 24 may also perform the following variable modification: for each specified period, based on the point cluster data input from the LiDAR device 10, the variable f(n) indicating that there are more than a specified number of voxels B in the first period or more below the specified period decreases by a specified decrease, and the variable f(n) indicating that there are fewer than a specified number of voxels B in the first period or more below the specified period increases by a specified increase.

[0107] In this case, as described in the above embodiment, when the predetermined increase and decrease are constant values ​​of 'a', and when the variable after step S4 is set to f(n+1), for voxels B representing points r where an object is detected in the first period or more, f(n+1) = f(n) - a; for voxels B representing points r where an object is detected in the first period or more, the number of voxels is less than the predetermined number, f(n+1) = f(n) + a. Therefore, the variable of voxels B representing the region where the object is located decreases, and the variable of voxels B representing the region where the object is not located increases.

[0108] As described above, step S7 is an output step that distinguishes voxels B whose variable f(n) is above a predetermined threshold t from other voxels B. Therefore, in this modified example, in step S7, voxels B assigned other labels are voxels B that can become background voxels, and voxels B assigned the predetermined labels can become other voxels B.

[0109] Furthermore, in this modified example, Modification 1 is as follows. As described above, in Modification 1, the assignment unit 23 determines the initial value f(0) for each voxel B during a specific period, based on the point group data and the number of points r that indicate that an object was detected in a second period below that specific period. In the case of Modification 1, which applies this modified example, this means that the more points r there are, the smaller the initial value f(0) is set by the assignment unit 23.

[0110] Furthermore, in the case of this modified example, Modification 2 is as follows. As described above, in Modification 2, the assignment unit 23, for each voxel B during a specific period, determines an initial value f(0) based on the point group data, according to the number of points indicating that an object was detected in a second period or more below the specific period and the duration of that point. In the case of Modification 2 applying this modified example, this means that the more points r there are and the longer the duration of points r, the smaller the initial value f(0) is set by the assignment unit 23.

[0111] Furthermore, in this modified example, Modification 4 is as follows. As described above, in Modification 4, at least one of the predetermined rise and predetermined fall amplitude is determined for each voxel B based on the number of points r that indicate the detection of an object during the predetermined period or more than the first period. In applying Modification 4 of this modified example, this means that when the number of points r is greater than or equal to the predetermined number, the greater the number of points r, the larger the fall amplitude is set; and when the number of points r is less than the predetermined number, the smaller the number of points r, the larger the rise amplitude is set.

[0112] Furthermore, in this modified example, Modification 5 is as follows. As described above, in Modification 5, at least one of the predetermined rise and predetermined fall amplitude is determined for each voxel B based on the number of points r that indicate the detection of an object during the predetermined period (above the first period) and the duration of those points r. This means that when the number of points r is greater than or equal to the predetermined number, the more points r there are and the longer the duration of those points r, the larger the fall amplitude will be set by the variable unit 24; conversely, when the number of points r is less than the predetermined number, the fewer points r there are and the shorter the duration of those points r, the larger the rise amplitude will be set by the variable unit 24.

[0113] As explained above, one aspect of the present invention based on Modification 6 is a control device 20 that processes point group data output from LiDAR device 10, comprising: a partitioning unit 22 that partitions spatial data into multiple voxels B; an assignment unit 23 that assigns an initial value f(0) to each voxel B; a variation unit 24 that, for each predetermined period, varies a variable f(n) based on point group data input from LiDAR device 10 such that the variable f(n) representing the presence of a predetermined number or more voxels B at a first period or longer than the predetermined period decreases by a predetermined decrease, and the variable f(n) representing the presence of fewer voxels B at a first period or longer than the predetermined number of voxels B at the first period or longer increases by a predetermined increase; and an output unit 25 that, after repeating the variation of variable f(n) in variation unit 24 a predetermined number of times, outputs data that distinguishes voxels B at a predetermined threshold t or higher of variable f(n) from other voxels B.

[0114] In addition, another aspect of the present invention based on Modification 6 is a program executed by a control device 20 that processes point group data output from the LiDAR device 10, causing the control device 20 to perform the following steps: dividing the data representing the space into multiple voxels B; assigning an initial value f(0) to each voxel B of variable f(n); for each specified period, based on the point group data input from the LiDAR device 10, changing the variable f(n) as follows: decreasing the variable f(n) by a specified decreasing margin for voxels B representing the presence of a specified number of points r where objects are detected in a first period or more below the specified period, and increasing the variable f(n) by a specified increasing margin for voxels B representing the presence of fewer points where objects are detected in a first period or more than the specified number; after repeating the step of changing the variable f(n) a specified number of times, outputting data that distinguishes voxels B with a variable f(n) of a specified threshold t or higher from other voxels B.

[0115] Another aspect of the present invention based on Modification 6 is a LiDAR system 1, which includes a LiDAR device 10 and a control device 20 that inputs point group data from the LiDAR device 10. The control device 20 includes: a partitioning unit 22 that partitions spatial data into multiple voxels B; an assignment unit 23 that assigns an initial value f(0) to each voxel B; a variation unit 24 that, for each predetermined period, varies a variable f(n) based on the point group data input from the LiDAR device 10 such that the variable f(n) representing the presence of a predetermined number or more voxels B at a first period or longer than the predetermined period decreases by a predetermined decrease, and the variable f(n) representing the presence of fewer voxels at a first period or longer than the predetermined number of voxels B increases by a predetermined increase; and an output unit 25 that, after repeating the variation of variable f(n) in the variation unit 24 a predetermined number of times, outputs data that distinguishes voxels B at a predetermined threshold t from other voxels B.

[0116] Based on the above, one aspect of the present invention is as follows. Specifically, one aspect of the present invention is a control device 20 that processes point group data output from a LiDAR device 10, comprising: a separation unit 22 that divides spatial data into multiple voxels B; an assignment unit 23 that assigns an initial value f(0) to each voxel B; a variation unit 24 that, for each predetermined period, based on point group data input from the LiDAR device 10, performs one of the following variations on a variable f(n): increasing by a predetermined increase or decreasing by a predetermined decrease, for the variable f(n) indicating that a predetermined number of voxels B are detected at a point r above or below the predetermined period; and performs the other variation on a variable f(n) indicating that a predetermined number of voxels B are detected at a point r below the predetermined number above the predetermined period; and an output unit 25 that, after repeating the variation of variable f(n) in the variation unit 24 a predetermined number of times, outputs data that distinguishes voxels B with a variable f(n) of a predetermined threshold t or higher from other voxels B.

[0117] Another aspect of the present invention is a program executed by a control device 20, which processes point cluster data output from the LiDAR device 10. This program causes the control device 20 to perform the following steps: dividing the spatial data into multiple voxels B; for each specified period, based on the point cluster data input from the LiDAR device 10, performing a change in variable f(n) that indicates the presence of a specified number or more voxels B at points r where objects are detected in a first period below the specified period, either increasing by a specified increase or decreasing by a specified decrease; performing a change in variable f(n) that indicates the presence of fewer voxels B at points r where objects are detected in the first period than the specified number, either increasing by a specified increase or decreasing by a specified decrease; repeating the step of changing variable f(n) a specified number of times, and then outputting data that distinguishes voxels B where variable f(n) is a specified threshold t or higher from other voxels B.

[0118] Another aspect of the present invention is a LiDAR system 1, which includes a LiDAR device 10 and a control device 20 that inputs point group data from the LiDAR device 10. The control device 20 includes: a partitioning unit 22 that partitions the data representing the space into a plurality of voxels B; an assignment unit 23 that assigns an initial value f(0) of a variable to each voxel B; and a variation unit 24 that, for each predetermined period, based on the point group data input from the LiDAR device 10, determines whether there are a predetermined number or more points r representing objects detected in a first period less than the predetermined period. The variable f(n) of voxel B undergoes one of the following changes: an increase of a predetermined increase or a decrease of a predetermined decrease. The variable f(n) of voxel B representing the number of points r in which objects are detected less than a predetermined number during the first period undergoes the other of the following changes: an increase of a predetermined increase or a decrease of a predetermined decrease. The output unit 25, after repeating the change of variable f(n) in the change unit 24 a predetermined number of times, outputs data that distinguishes voxel B where variable f(n) is above a predetermined threshold t from other voxel B.

[0119] According to the present invention, a control device, a program, and a LiDAR system are provided that can distinguish data that can be used as background data from other data even when the moving body is in the monitoring area, and can be used in the fields of indoor and outdoor monitoring systems.

Claims

1. A control device for processing point cluster data output from a LiDAR device, characterized in that, have: The partitioning section divides the data representing the space into multiple voxels; The assignment unit assigns initial values ​​to variables for each of the voxels; The variable unit, for each specified period, based on the point cluster data input from the LiDAR device, performs one of the following changes on the variable: either increasing by a specified increase or decreasing by a specified decrease, for the voxel indicating that there are more than a specified number of points where an object is detected in the first period or more below the specified period; and performs the other of the following changes on the variable: either increasing by a specified increase or decreasing by a specified decrease, for the voxel indicating that there are fewer than the specified number of points where an object is detected in the first period or more. The output unit, after repeating the variation of the variable in the variation unit a predetermined number of times, outputs data that distinguishes the voxels whose variable is above a predetermined threshold from the voxels that are not.

2. The control device according to claim 1, characterized in that, The initial value is determined, within a specific period, for each of the individual voxels, based on the point group data, according to the number of points indicating that an object was detected above a second period below the specific period.

3. The control device according to claim 1, characterized in that, The initial value is determined, within a specific period, for each of the individual voxels, based on the point group data, according to the number of points indicating that an object was detected above a second period below the specific period and the duration of that point.

4. The control device according to claim 2 or 3, characterized in that, The length of the specific period is equal to the length of the specified period.

5. The control device according to claim 4, characterized in that, The length of the second period is equal to the length of the first period.

6. The control device according to claim 1, characterized in that, The initial value is the same in each of the voxels.

7. The control device according to claim 6, characterized in that, The initial value is the specified threshold.

8. The control device according to claim 1, characterized in that, The specified quantity is 1.

9. The control device according to claim 1, characterized in that, The specified quantity is 2 or more.

10. The control device according to claim 1, characterized in that, At least one of the specified increase range and the specified decrease range is a constant value.

11. The control device according to claim 1, characterized in that, At least one of the specified rise and the specified fall is determined for each of the individual voxels based on the number of points indicating that an object was detected during the first period within the specified period.

12. The control device according to claim 1, characterized in that, At least one of the specified rise and the specified fall is for each of the individual voxels and is determined based on the number of points indicating that an object was detected above the first period and the duration of that point within the specified period.

13. The control device according to claim 1, characterized in that, The length of the first period is the same as the length of the specified period.

14. A program executed by a control device that processes point cluster data output from a LiDAR device, characterized in that, The control device performs the following steps: Divide the data representing the space into multiple voxels; Assign initial values ​​to the variables for each of the aforementioned voxels; For each specified period, based on the point cluster data input from the LiDAR device, the variable representing the presence of a specified number or more of voxels where objects are detected in a first period below the specified period is subject to either a specified increase or a specified decrease; the variable representing the presence of fewer voxels where objects are detected in a first period than the specified number is subject to either a specified increase or a specified decrease. After repeating the step of changing the variable a predetermined number of times, output data that distinguishes the voxels whose variable is above a predetermined threshold from the voxels that are otherwise.

15. A LiDAR system comprising a LiDAR device and a control device for inputting point cluster data from said LiDAR device, characterized in that, The control device has: The partitioning section divides the data representing the space into multiple voxels; The assignment unit assigns initial values ​​to variables for each of the voxels; The variable unit, for each specified period, based on the point cluster data input from the LiDAR device, performs one of the following changes on the variable: either increasing by a specified increase or decreasing by a specified decrease, for the voxel indicating that there are more than a specified number of points where an object is detected in the first period or more below the specified period; and performs the other of the following changes on the variable: either increasing by a specified increase or decreasing by a specified decrease, for the voxel indicating that there are fewer than the specified number of points where an object is detected in the first period or more. The output unit, after repeating the variation of the variable in the variation unit a predetermined number of times, outputs data that distinguishes the voxels whose variable is above a predetermined threshold from the voxels that are not.

Images