Dynamic obstacle recognition method and device, storage medium and electronic device
By collecting point clouds on cleaning equipment and mapping them to local maps for clustering and obstacle identification, the problem of poor timeliness in dynamic obstacle identification in existing technologies is solved, achieving more efficient dynamic obstacle identification and obstacle avoidance.
CN122391861APending Publication Date: 2026-07-14DREAM INNOVATION TECH (SUZHOU) CO LTD
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- DREAM INNOVATION TECH (SUZHOU) CO LTD
- Filing Date
- 2022-05-20
- Publication Date
- 2026-07-14
AI Technical Summary
Technical Problem
In existing technologies, dynamic obstacle recognition suffers from poor timeliness due to the large amount of data, which increases the risk of collisions between cleaning equipment and dynamic obstacles.
Method used
Point clouds are continuously collected by target sensors on the cleaning equipment. Points above the ground are mapped to local maps, clustering operations are performed to identify obstacles, and dynamic obstacles are determined based on their positions in multiple local maps.
Benefits of technology
It improves the timeliness and accuracy of dynamic obstacle recognition, reduces the risk of collisions between cleaning equipment and dynamic obstacles, and enhances operational safety.
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Smart Images
Figure CN122391861A_ABST
Abstract
The application provides a dynamic obstacle identification method and device, a storage medium and an electronic device. The method comprises: continuously collecting point clouds of a collection area of a target sensor on a cleaning device through the target sensor to obtain multiple frames of point clouds; mapping points with a height above the ground and less than or equal to a first height threshold to a graph according to each frame of point clouds of the multiple frames of point clouds to obtain multiple local graphs; determining obstacles contained in each local graph based on the multiple local graphs; and sequentially identifying the obstacles contained in each local graph to obtain dynamic obstacles in the collection area. The application solves the problem of poor timeliness of dynamic obstacle identification caused by a large amount of data to be processed in the related art.
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