Research and Application on 3D Laser Point Cloud Clustering Method Based on Image Detection
To address the need for mobile robots to perceive obstacles in surrounding environment,especially the semantic detection and size recognition of LiDAR point cloud data,a method that integrates image processing and point cloud data processing is proposed.Firstly,the extrinsic parameters of the 3D LiDAR and stereo camera are calibrated.Then,the YOLOv4 deep neural network is used to detect 2D image instances and recover pixel depth through the stereo camera,mapping the detected targets to the LiDAR point cloud.After filtering and applying geometric constraints to the point cloud,a KD-tree-based search method is used for Euclidean clustering segmentation of the point cloud.Finally,the identified semantic information is output to the point cloud clustering results.Experimental results show that the designed method can accurately and quickly identify and segment point cloud clusters,making it applicable to mobile robot navigation and obstacle avoidance.
Point cloud clusteringobject detectionbinocular vision positioning
NETL
NSTL
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