A Fast Segmentation Algorithm for 3D Point Cloud on Inclined Ground
The methods that rely on plane fitting or local geometric features to distinguish ground obstacles are widely used in the field of automatic driving,but their performance could be reduced in the case of sloping terrain or sparse data.In a sloping terrain scene,the point cloud data provided by LiDAR,the grid map method through 3D projection and line fitting of the ground point cloudare used to reduce the computational complexity and realize the segmentation of the ground point cloud.For the collection of non-ground point clouds,SLR clustering algorithm is used to process.Ground points and non-ground points are distinguished in the vertical direction by setting the intensity feature threshold,and the scanned obstacle ground is classified.Through experimental analysis,the proposed algorithm has better mapping effect than other ground point cloud segmentation algorithms in sloping terrain.On the other hand,the intensity features processed by SLR clustering algorithm are more accurate in the coverage of X,Y and Z directions.For example,in the X direction,compared with the fast ground segmentation algorithm,the average increase is 44.0％;compared with the algorithm with grid map,the average increase is 40.1％.
万方数据
维普
