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CN-121994204-A - Map construction method, navigation method and device of robot, equipment and storage medium

CN121994204ACN 121994204 ACN121994204 ACN 121994204ACN-121994204-A

Abstract

The embodiment of the application provides a map construction method, a device, equipment and a storage medium for a robot, wherein the map construction method comprises the steps of obtaining at least one key frame point cloud obtained by moving the robot in a target scene and key frame pose corresponding to each key frame point cloud, obtaining a current frame point cloud obtained by the robot, determining loop detection parameters based on the point cloud density of the current frame point cloud, carrying out loop detection on the current frame point cloud based on the loop detection parameters to obtain loop detection results, updating the key frame pose corresponding to each key frame point cloud based on the loop detection results, and constructing a three-dimensional point cloud map based on the at least one key frame point cloud and the updated key frame pose corresponding to each key frame point cloud. Therefore, the accuracy and the robustness of loop detection under different scenes can be improved, and the accuracy and the robustness of map construction are improved.

Inventors

  • ZHANG ZHICHAO
  • SUN ZHENYANG

Assignees

  • 宁德时代新能源科技股份有限公司
  • 宁德时代未来能源(上海)研究院有限公司

Dates

Publication Date
20260508
Application Date
20241106

Claims (16)

  1. 1. A method of map construction, the method comprising: acquiring at least one acquired key frame point cloud and key frame pose corresponding to each key frame point cloud of a robot moving in a target scene; Acquiring a current frame point cloud acquired by the robot, and determining loop detection parameters based on the point cloud density of the current frame point cloud; Performing loop detection on the current frame point cloud based on the loop detection parameters to obtain a loop detection result, and updating the key frame pose corresponding to each key frame point cloud based on the loop detection result; and constructing a three-dimensional point cloud map based on the at least one key frame point cloud and the updated key frame pose corresponding to each key frame point cloud.
  2. 2. The map construction method according to claim 1, wherein the determining loop detection parameters based on the point cloud density of the current frame point cloud includes: determining loop detection parameters based on the size relation between the point cloud density of the current frame point cloud and a density threshold value; The loop detection parameters comprise at least one descriptor construction parameter and a loop matching threshold, wherein the descriptor construction parameter is used for constructing a point cloud descriptor of the point cloud of the current frame and a point cloud descriptor of each key frame point cloud, and the loop matching threshold is used for matching the point cloud descriptor of the point cloud of the current frame with the point cloud descriptor of the key frame point cloud to determine that the point cloud of the current frame is a loop frame.
  3. 3. The map construction method according to claim 2, wherein the loop detection parameter includes the descriptor construction parameter including a descriptor extraction range; The determining loop detection parameters based on the size relation between the point cloud density of the current frame point cloud and a density threshold value comprises at least one of the following steps: Determining the descriptor extraction range as a first extraction range under the condition that the point cloud density of the point cloud of the current frame is smaller than a first density threshold; Determining the extraction range of the descriptor as a second extraction range under the condition that the point cloud density of the point cloud of the current frame is greater than or equal to a first density threshold; Wherein the first extraction range is greater than the second extraction range.
  4. 4. The map construction method according to claim 2, wherein the loop detection parameter includes a loop matching threshold; The determining loop detection parameters based on the size relation between the point cloud density of the current frame point cloud and a density threshold value comprises at least one of the following steps: Determining the loop matching threshold value as a first matching threshold value under the condition that the point cloud density of the current frame point cloud is smaller than a second density threshold value; determining the loop matching threshold value as a second matching threshold value under the condition that the point cloud density of the current frame point cloud is larger than or equal to a second density threshold value; Wherein the first match threshold is less than the second match threshold.
  5. 5. The map construction method according to claim 1, wherein the acquiring the current frame point cloud acquired by the robot includes: determining current motion information of the robot based on current inertial measurement data acquired by the robot; and under the condition that the current motion information meets the target motion condition, acquiring the current frame point cloud acquired by the robot.
  6. 6. The map construction method according to claim 5, wherein the current motion information includes a current motion speed, and wherein the acquiring the current frame point cloud acquired by the robot in the case where the current motion information satisfies a target motion condition includes: determining a point cloud frame extraction frequency based on the current motion speed when the current motion speed is greater than or equal to a speed threshold; and extracting the point cloud of the current frame from the point cloud data acquired by the robot according to the point cloud frame extraction frequency.
  7. 7. The map construction method according to claim 5, wherein the current motion information includes a degree of pose change of a current pose of the robot with respect to a previous key frame pose, the previous key frame pose being a key frame pose corresponding to a previous key frame point cloud; Under the condition that the current motion information meets the target motion condition, acquiring the current frame point cloud acquired by the robot comprises the following steps: and under the condition that the pose change degree is greater than or equal to a degree threshold value, acquiring the current frame point cloud acquired by the robot.
  8. 8. The map construction method according to any one of claims 1 to 7, characterized in that the method further comprises: and determining the current frame point cloud as a new key frame point cloud, and determining the current frame pose corresponding to the current frame point cloud as the key frame pose corresponding to the new key frame point cloud.
  9. 9. The map construction method according to any one of claims 1 to 7, characterized in that the method further comprises: carrying out three-dimensional grid division on a three-dimensional point cloud space corresponding to the three-dimensional point cloud map, and carrying out dynamic obstacle filtering on the three-dimensional point cloud map according to the occupation state of the point clouds in each divided three-dimensional grid to obtain a static three-dimensional point cloud map; And generating a two-dimensional grid map of the target scene based on the static three-dimensional point cloud map.
  10. 10. The map construction method according to claim 9, wherein the three-dimensional grid division of the three-dimensional point cloud space corresponding to the three-dimensional point cloud map includes: And carrying out three-dimensional grid division on the three-dimensional point cloud space based on the point cloud density distribution of the three-dimensional point cloud space, wherein the grid size corresponding to the space region with smaller point cloud density in the three-dimensional point cloud space is larger.
  11. 11. A method of navigating a robot, the method comprising: Acquiring real-time point cloud data acquired by a robot moving in a target scene and a three-dimensional point cloud map of the target scene, wherein the three-dimensional point cloud map is constructed by adopting the map construction method according to any one of claims 1 to 10; determining the real-time pose of the robot based on the real-time point cloud data; And carrying out navigation planning on the robot based on the real-time pose and the three-dimensional point cloud map to obtain a navigation result.
  12. 12. A map construction apparatus, characterized by comprising: The first acquisition module is used for acquiring at least one acquired key frame point cloud and key frame pose corresponding to each key frame point cloud when the robot moves in a target scene; the second acquisition module is used for acquiring the current frame point cloud acquired by the robot and determining loop detection parameters based on the point cloud density of the current frame point cloud; The loop detection module is used for carrying out loop detection on the current frame point cloud based on the loop detection parameters to obtain a loop detection result, and updating the key frame pose corresponding to each key frame point cloud based on the loop detection result; And the construction module is used for constructing a three-dimensional point cloud map based on the at least one key frame point cloud and the updated key frame pose corresponding to each key frame point cloud.
  13. 13. A navigation device for a robot, comprising: A third acquisition module for acquiring real-time point cloud data acquired by a robot moving in a target scene and a three-dimensional point cloud map of the target scene, wherein the three-dimensional point cloud map is constructed by adopting the map construction method according to any one of claims 1 to 10; the second determining module is used for determining the real-time pose of the robot based on the real-time point cloud data; And the navigation module is used for carrying out navigation planning on the robot based on the real-time pose and the three-dimensional point cloud map to obtain a navigation result.
  14. 14. A computer device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that the processor implements the steps of the method of any of claims 1 to 11 when the program is executed.
  15. 15. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 11.
  16. 16. A computer program product comprising a computer program or instructions which, when executed by a processor, carries out the steps of the method of any one of claims 1 to 11.

Description

Map construction method, navigation method and device of robot, equipment and storage medium Technical Field The present application relates to the field of robots, and in particular, to a method and apparatus for map construction and navigation of a robot, a device, and a storage medium. Background This section is intended to provide a background or context for embodiments of the application. The description herein is not admitted to be prior art by inclusion in this section. Mobile robots are increasingly used in life and industry, for example, humanoid indoor service robots, sweeping robots, etc. Map construction and positioning navigation are key to enabling autonomous movement of a robot. However, in the related art, the map construction scheme of the robot is difficult to meet the map construction positioning requirement of a complex scene, and the accuracy and the robustness of constructing the map are affected. Disclosure of Invention In view of this, the embodiments of the present application expect to provide a map construction method, a navigation device, and a navigation storage medium, which can adapt to corresponding loop detection parameters according to point cloud densities of different scenes, so as to adapt to map construction positioning requirements of different scenes, improve accuracy and robustness of loop detection in different scenes, further improve accuracy and robustness of map construction, and have wider application scenes. The technical scheme of the embodiment of the application is realized as follows: The embodiment of the application provides a map construction method, which comprises the following steps: Acquiring at least one acquired key frame point cloud and a key frame pose corresponding to each key frame point cloud of a robot moving in a target scene; acquiring a current frame point cloud acquired by a robot, and determining loop detection parameters based on the point cloud density of the current frame point cloud; performing loop detection on the current frame point cloud based on the loop detection parameters to obtain a loop detection result, and updating the key frame pose corresponding to each key frame point cloud based on the loop detection result; And constructing a three-dimensional point cloud map based on at least one key frame point cloud and the updated key frame pose corresponding to each key frame point cloud. The map construction method comprises the steps of firstly, obtaining at least one key frame point cloud obtained by a robot moving in a target scene and key frame pose corresponding to each key frame point cloud, then, determining loop detection parameters based on the point cloud density of the current frame point cloud obtained by the robot, then, carrying out loop detection on the current frame point cloud based on the loop detection parameters, updating the key frame pose corresponding to each key frame point cloud based on a loop detection result, and finally, constructing a three-dimensional point cloud map based on at least one key frame point cloud and the updated key frame pose corresponding to each key frame point cloud. Therefore, the loop detection parameters adopted in the loop detection process are determined based on the point cloud density of the current frame point cloud acquired by the robot, so that the corresponding loop detection parameters can be adapted according to the point cloud density of different scenes to adapt to the map building and positioning requirements of the different scenes, the accuracy and the robustness of loop detection in the different scenes are improved, the accuracy and the robustness of map building are further improved, and the method has wider application scenes. In some embodiments, determining a loop detection parameter based on the point cloud density of the current frame point cloud comprises determining the loop detection parameter based on a magnitude relation between the point cloud density of the current frame point cloud and a density threshold, wherein the loop detection parameter comprises at least one of a descriptor construction parameter and a loop matching threshold, the descriptor construction parameter is used for constructing a point cloud descriptor of the current frame point cloud and a point cloud descriptor of each key frame point cloud, and the loop matching threshold is used for matching the point cloud descriptor of the current frame point cloud with the point cloud descriptor of the key frame point cloud to determine that the current frame point cloud is a loop frame. In the above embodiment, according to the size relationship between the point cloud density and the density threshold of the point cloud of the current frame, the descriptor construction parameters and/or the loop matching threshold adopted in the loop detection process are determined, so that the accuracy and the robustness of loop detection under different scenes can be further improved, and the accurac