Covariance Based Terrain Mapping for Autonomous Mobile Robots

Autonomous mobile robots operating in unknown terrain have to guide their drive decisions through perception. Local mapping and traversability analysis are essential for safe rover operation and low level locomotion. This paper deals with the challenge of building a local, robot-centric map from onboard range sensing. We introduce a new highly-efficient converging covariance based map update for estimating a digital elevation map. The map is capable of representing sub-cell size obstacles and can be queried for height, the presence of obstacles, and slope in constant time. Extensive experiments in simulation and real world test campaigns are used to evaluate the performance of our approach. The presented mapping technique outperforms state of the art grid-mapping algorithms for computationally constraint systems in run time, memory usage, precision and resolvable obstacle size. Further application scenarios are discussed. A variant of this map approach is projected to be used on NASA’s 2024 CADRE mission to the Moon.