Plane segmentation and fitting method of point clouds based on improved density clustering algorithm for laser radar

摘要

Plane segmentation and fitting method of point clouds based on improved density clustering algorithm is put forward. We proposed the plane segmentation and fitting framework, which comprises of four steps: coordinate transformation, filtering, coarse segmentation, fine segmentation, plane fitting. The global coordinates of laser radar are deduced. Abnormal points are removed using statistical filtering based on Gaussian distribution. After filtering, Point clouds are segmented roughly adopting improved density clustering algorithm with proposed threshold, which is originally related to the resolution of laser radar. The point clouds are segmented furthermore with normal vector, which could make up for shortcomings, which are over-segmentation and under segmentation. Finally planes are fitted with normal vector and centroid point. The laser radar was designed, and plane segmentations and fitting were carried out. The experimental results show that it is effective and automatic for plane segmentation with proposed method.