Gaussian Process-Based Traversability Analysis for Terrain Mapless Navigation

Efficient navigation through uneven terrain re- mains a challenging endeavor for autonomous robots. We pro- pose a new geometric-based uneven terrain mapless navigation framework combining a Sparse Gaussian Process (SGP) local map with a Rapidly-Exploring Random Tree* (RRT*) planner. Our approach begins with the generation of a high-resolution SGP local map, providing an interpolated representation of the robot’s immediate environment. This map captures crucial envi- ronmental variations, including height, uncertainties, and slope characteristics. Subsequently, we construct a traversability map based on the SGP representation to guide our planning process. The RRT* planner efficiently generates real-time navigation paths, avoiding untraversable terrain in pursuit of the goal. This combination of SGP-based terrain interpretation and RRT* planning enables ground robots to safely navigate environments with varying elevations and steep obstacles. We evaluate the performance of our proposed approach through robust sim- ulation testing, highlighting its effectiveness in achieving safe and efficient navigation compared to existing methods. See the project GitHub1 for source code and supplementary materials, including a video demonstrating experimental results.