Point cloud segmentation based on Euclidean clustering and multi-plane extraction in rugged field
Haoyue Liu, Rui Song, Xuebo Zhang, Hui Liu
Abstract
Abstract In this paper, a novel method for point cloud segmentation based on Euclidean clustering and multi-plane extraction is proposed. To cope with overhanging objects, such as tree branches, a hybrid elevation map assisted by Euclidean clustering is designed. By clustering the 3D point clouds falling into the grid cell, the obstacles above a free space are checked and the corresponding traversable regions below are identified. Furthermore, the time consumption is reduced for segmentation by using the multi-resolution grid method. In addition, the multi-plane extraction method based on random sample consensus is well adapted to non-flat terrain. In the simulation, a variety of virtual environments are built on the Gazebo platform to demonstrate the performance of the proposed algorithm. Moreover, it is also evaluated in outdoor environments. The results show that the accuracy as well as efficiency of point cloud segmentation achieves superior performance over existing approaches.