Abstract
Ground point detection is an important part of unmanned driving technology, and the detection results will affect the subsequent tasks. Aiming at the problem of under-segmentation and mis-segmentation of lidar ground point segmentation in unstructured scenes, a ground point recognition method based on grid division is proposed. This method first uses the constraint conditions to preprocess and extract the ROI region; secondly, the point cloud is divided by the method of grid division; finally, the plane fitting method is used to realize the ground point recognition. Based on the structured data set KITTI and the unstructured data set ORFD data set, this paper compares them with RANSAC, LF and HDL algorithms. The experimental results show that the algorithm in this paper can segment the ground points well, which is superior to the three algorithms compared in the evaluation index, and can detect the ground points quickly and accurately.
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