  Efficient navigation through uneven terrain remains a challenging endeavor
for autonomous robots. We propose 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
environmental 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 simulation testing, highlighting its effectiveness in
achieving safe and efficient navigation compared to existing methods.
