GEM: Online Globally Consistent Dense Elevation Mapping for Unstructured Terrain
Yiyuan Pan, Xuecheng Xu, Xiaqing Ding, Shoudong Huang, Yue Wang, Rong Xiong
Abstract
Online dense mapping gives a representation of the unstructured terrain, which is indispensable for safe robotic motion planning. In this article, we propose such an elevation mapping system, namely GEM, to generate a dense local elevation map in constant real time for fast responsive local planning, and maintain a globally consistent dense map for path routing at the same time. We model the global elevation map as a collection of submaps. When the trajectory estimation of the robot is corrected by simultaneous localization and mapping (SLAM), only relative poses between submaps are updated without rebuilding the submap. As a result, this deformable global dense map representation is able to keep the global consistency online. Besides, we accelerate the local mapping by integrating traversability analysis into the mapping system to save the computation cost by obstacle awareness. The system is implemented by CPU-GPU coordinated processing to guarantee constant real-time performance for in-time handling of dynamic obstacles. Substantial experimental results on both simulated and real-world data set validate the efficiency and effectiveness of GEM.