Robot Ground Laser Point Clouds Segmentation Algorithm Based on Adaptive Slope Threshold

In view of the problems of sensor installation error, poor real-time performance and low accuracy of ground point cloud segmentation in robot obstacle detection technology based on Lidar, a robot ground laser point cloud segmentation algorithm based on adaptive slope threshold is proposed. First, the extrinsic parameter calibration algorithm of the Lidar and robot body coordinate system based on trajectory matching is used to improve the extrinsic parameter installation accuracy of the Lidar; secondly, in order to improve the real-time performance of ground point cloud segmentation, the original laser point cloud is preprocessed; then, In order to improve the efficiency and accuracy of point cloud segmentation, polar coordinate mapping is used to order the laser point cloud, and the idea of adaptive slope threshold is used to complete the reliable segmentation of ground point cloud; finally, an experimental comparative analysis is conducted. The results show that under the same environment, the segmentation efficiency and accuracy of this algorithm are higher.