Covariance Based Terrain Mapping for Autonomous Mobile Robots

Abstract

In this paper, we present a local, robot-centric navigation map optimized for autonomous mobile robots operating in unknown environments, enhancing their onboard perception systems for collision-free operation with far look-ahead distances. Utilizing a novel converging covariance cell representation, our approach effectively analyzes hazards such as obstacles and hazardous slopes in both terrestrial and aerial navigation contexts. The new technique specifically targets mapping from stereo scenarios with ultra short baseline and highly oblique viewpoints close to the ground.Our methodology surpasses traditional window-based hazard analysis by resolving sub-cell size obstacles and terrain gradients at the individual cell level, thereby avoiding the computational overhead typically associated with such analyses. It leverages a multi-resolution strategy adaptive to the range errors common in stereo vision systems, making it particularly suitable for embedded systems with computational limitations.Functionality includes constant-time queries for height, obstacle presence, and slope details, boasting improvements in run time, memory usage, precision, and resolvable obstacle size compared to existing grid-based mapping algorithms. We validate our approach through rigorous simulation and real-world testing. This technique will be used for the local mapping and collision avoidance on NASA’s CADRE lunar rovers.