Spatial Multi-feature Segmentation of 3D Lidar Point Cloud

Multi-layer solid-state lidar has become an important tool for environment perception of unmanned platform, and has been widely used in vehicle-mounted environment modeling.Due to the low resolution of lidar, the sensitivity of environmental noise, and the complexity of the scene, the fast and effective segmentation of the scene becomes a key problem in the real-time environment modeling.In view of the obvious curvature difference between buildings and vegetation in the actual collected point cloud data, this paper proposes an improved fast segmentation method of 3D point cloud based on multi-layer lidar.After the initial segmentation of building facade is realized based on curvature segmentation, the weighted Euclidean clustering is used for the second iterative segmentation of the initial segmented point cloud, which can reduce the iterative process and avoid falling into local optimum.Through the unmanned platform data acquisition and processing experiments and public data experiments, the effectiveness of