Point cloud segmentation based on Euclidean clustering and multi-plane extraction in rugged field

Abstract

In this paper, a novel method of point clouds segmentation based on Euclidean clustering and multi-plane extraction is newly proposed. To cope with overhanging objects, such as tree branches, a hybrid elevation map assisted with 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 the segementation by using the multi-resolution grids method. In addition, the multiplane extraction method based on RANSAC is well adapted to non-flat terrain. In the simulation, a variety of virtual environments are built on Gazebo platform to demonstrate the performance of the proposed algorithm. Moreover, it is also evaluated in the field environments. The results show that the accuracy as well as efficiency of point clouds segmentation achieves superior performance over existing approaches.