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JP-7855108-B1 - Background data management device, background data management method, and background data management program

JP7855108B1JP 7855108 B1JP7855108 B1JP 7855108B1JP-7855108-B1

Abstract

[Challenge] To enable the preparation of appropriate background point cloud data. [Solution] The point cloud acquisition unit 21 acquires point cloud data containing data from multiple points obtained by irradiating a target area with illumination light and receiving reflected light reflected at reflection points, and records the acquired point cloud data together with identification information for that point cloud data. The reference background generation unit 22 generates a single point cloud data as background point cloud data, which contains the data of each point included in the multiple point cloud data acquired by the point cloud acquisition unit 21 at different times. [Selection Diagram] Figure 17

Inventors

  • 阿部 紘和
  • 中尾 尭理
  • 井ノ口 裕也
  • 山足 光義

Assignees

  • 三菱電機デジタルイノベーション株式会社

Dates

Publication Date
20260507
Application Date
20250324

Claims (8)

  1. A point cloud acquisition unit acquires point cloud data containing data from multiple points obtained by irradiating a target area with light and receiving the reflected light reflected at the reflection points, and records the acquired point cloud data together with identification information for that point cloud data. A reference background generation unit generates a single point cloud data set as background point cloud data, which includes the data of each point included in multiple point cloud data sets acquired at different times by the point cloud acquisition unit . A deletion point extraction unit extracts deletion point data, which is data of points included in the point cloud data that satisfies the conditions from the point cloud data that formed the basis of the background point cloud data. A background update unit that deletes the deleted point data extracted by the deleted point extraction unit from the background point cloud data, A voxelization unit generates voxel data corresponding to the point cloud data by associating each point of the point cloud data acquired by the point cloud acquisition unit with the voxel containing the position of the target point among a plurality of voxels obtained by dividing the target region, and using each point of the point cloud data acquired by the point cloud acquisition unit as the target point, and by aligning the target point with the voxel containing the position of the target point among a plurality of voxels obtained by dividing the target region, and Equipped with , The deletion point extraction unit is a background data management device that extracts data of points included in point cloud data that is the basis of the background point cloud data, where the number of voxels that differ between the voxel data generated by the voxelization unit for that point cloud data and the voxel data generated by the voxelization unit for newly acquired point cloud data by the point cloud acquisition unit is equal to or greater than a standard number, as the deletion point data .
  2. The point cloud acquisition unit records the time the point cloud data was acquired as identification information. The background data management device according to claim 1 , wherein the deletion point extraction unit extracts data of points included in the point cloud data whose acquired time satisfies the conditions as the deletion point data.
  3. The background data management device according to claim 2 , wherein the deletion point extraction unit extracts data of points included in the point cloud data whose acquired time is earlier than or equal to a reference time as the deletion point data.
  4. The background data management device according to claim 2 , wherein the deletion point extraction unit extracts data of points included in the point cloud data whose acquired time falls within the target period as the deletion point data.
  5. The background data management device further, An additional point extraction unit extracts additional point data, which is data of points to be added to the background point cloud data, based on the difference between the point cloud data newly acquired by the point cloud acquisition unit and the background point cloud data generated by the reference background generation unit. The background data management device according to claim 1, further comprising a background update unit that adds the additional point data extracted by the additional point extraction unit to the background point cloud data.
  6. The background data management device further, The background data management device according to claim 1, further comprising a display unit for displaying data for each point included in the background point cloud data, the display unit for distinguishing and displaying data for points included in the point cloud data corresponding to the specified identification information.
  7. The computer acquires point cloud data containing data from multiple points obtained by irradiating a target area with light and receiving the reflected light reflected at the reflection points, and records the acquired point cloud data along with its identification information. The computer generates a single point cloud data set containing the data of each point in multiple point cloud data sets acquired at different times, and uses this as background point cloud data . The computer extracts the data of points to be deleted from the point cloud data that formed the basis of the background point cloud data, which are points that satisfy the conditions. The computer deletes the deleted point data from the background point cloud data. The computer generates voxel data corresponding to the point cloud data by associating each point in the point cloud data with a voxel containing the location of the target point among a plurality of voxels obtained by dividing the target region, A background data management method in which a computer extracts data of points included in point cloud data that is the basis for the background point cloud data, where the number of voxels that differ between the voxel data generated for the point cloud data and the voxel data generated for the newly acquired point cloud data is equal to or greater than a certain threshold, as the deleted point data .
  8. A point cloud acquisition process that obtains point cloud data containing data from multiple points obtained by irradiating a target area with light and receiving the reflected light reflected at reflection points, and records the acquired point cloud data along with identification information for that point cloud data, A reference background generation process generates a single point cloud data set containing the data of each point included in multiple point cloud data sets acquired at different times by the aforementioned point cloud acquisition process, as background point cloud data . A deletion point extraction process extracts deletion point data, which is the data of points included in the point cloud data that satisfies the conditions from the point cloud data that formed the basis of the aforementioned background point cloud data. A background update process that deletes the deleted point data extracted by the deletion point extraction process from the background point cloud data, A voxelization process generates voxel data corresponding to the point cloud data by associating each point in the point cloud data acquired by the point cloud acquisition process with the voxel containing the position of the target point among a plurality of voxels obtained by dividing the target region, and using each point in the point cloud data acquired by the point cloud acquisition process as the target point, and by associating the target point with the voxel containing the position of the target point among a plurality of voxels obtained by dividing the target region, and The computer functions as a background data update device to perform this task . The background data management program extracts, as the deleted point data, the data of points included in the point cloud data that formed the basis of the background point cloud data, where the number of voxels that differ between the voxel data generated by the voxelization process for that point cloud data and the voxel data generated by the voxelization process for the newly acquired point cloud data is equal to or greater than a certain number .

Description

This disclosure relates to a technology for detecting objects using point cloud data. There is a technology that uses visible light cameras to detect objects such as discarded objects. However, when using visible light cameras to detect objects, they cannot properly detect objects in environments with insufficient light, such as at night, or in environments where direct sunlight shines on the visible light camera, causing flare. There are optical sensors, such as LiDAR, that collect point cloud data by irradiating an object with light and receiving the reflected light at the reflection point. LiDAR stands for Light Detection and Ranging. Optical sensors can obtain point cloud data even in environments with insufficient light, such as at night, and in environments where direct sunlight shines into a visible light camera, causing flare. Therefore, detecting objects based on point cloud data obtained using optical sensors is being considered. Patent Document 1 describes a method for identifying the appearance of an object by comparing background point cloud data stored in memory with the current point cloud data and detecting any difference. Japanese Patent Publication No. 2020-118619 Configuration diagram of the background data management device 10 according to Embodiment 1.An explanatory diagram of the voxel definition 31 according to Embodiment 1.Flowchart of the background generation process according to Embodiment 1.An explanatory diagram of the reference background 42 according to Embodiment 1.An explanatory diagram of the voxelization method according to Embodiment 1.A flowchart of the background update process according to Embodiment 1.An explanatory diagram of the difference identification process according to Embodiment 1.Diagram illustrating the filtering process according to Embodiment 1.An explanatory diagram of the background candidate extraction process according to Embodiment 1.Configuration diagram of the background data management device 10 according to modified example 2.Flowchart of the background update process related to Modification Example 2.Configuration diagram of the background data management device 10 according to modified example 3.An explanatory diagram of the display content related to the modified example 3.An explanatory diagram of the display content related to the modified example 4.An explanatory diagram of the background candidate extraction process related to Modification 5.An explanatory diagram of the background candidate extraction process related to Modification 5.Configuration diagram of the background data management device 10 according to Embodiment 2.A flowchart of the background update process according to Embodiment 2.A diagram illustrating the effects of Embodiment 2.A diagram illustrating the effects of Embodiment 2.An explanatory diagram of the display content related to the modified example 8.An explanatory diagram of the display content related to the modified example 8. Embodiment 1. ***Explanation of the structure*** Referring to Figure 1, the configuration of the background data management device 10 according to Embodiment 1 will be described. The background data management device 10 is a computer. The background data management device 10 comprises hardware including a processor 11, memory 12, storage 13, and a communication interface 14. The processor 11 is connected to the other hardware via signal lines and controls this other hardware. Processor 11 is an integrated circuit (IC) that performs processing. IC stands for Integrated Circuit. Specific examples of processors 11 include CPUs, DSPs, and GPUs. CPU stands for Central Processing Unit. DSP stands for Digital Signal Processor. GPU stands for Graphics Processing Unit. Memory 12 is a storage device that temporarily stores data. Specific examples of memory 12 include SRAM and DRAM. SRAM stands for Static Random Access Memory. DRAM stands for Dynamic Random Access Memory. Storage 13 is a storage device for storing data. Specific examples of storage 13 include HDDs and SSDs. HDD stands for Hard Disk Drive. SSD stands for Solid State Drive. Storage 13 may also be a portable recording medium such as an SD® memory card, CompactFlash®, NAND flash, flexible disk, optical disk, compact disk, Blu-ray® disc, or DVD. SD stands for Secure Digital. DVD stands for Digital Versatile Disk. The communication interface 14 is an interface for communicating with external devices. Specific examples of the communication interface 14 include Ethernet®, USB, and HDMI® ports. USB stands for Universal Serial Bus. HDMI stands for High-Definition Multimedia Interface. The background data management device 10 is connected to the optical sensor 40 via a communication interface 14. The optical sensor 40 is a device that acquires point cloud data 41 by irradiating a target area with light and receiving the reflected light at the reflection points. The position of each point in the point cloud data 41 is determined from the irradiat