  We propose a new method for autonomous navigation in uneven terrains by
utilizing a sparse Gaussian Process (SGP) based local perception model. The SGP
local perception model is trained on local ranging observation (pointcloud) to
learn the terrain elevation profile and extract the feasible navigation
subgoals around the robot. Subsequently, a cost function, which prioritizes the
safety of the robot in terms of keeping the robot's roll and pitch angles
bounded within a specified range, is used to select a safety-aware subgoal that
leads the robot to its final destination. The algorithm is designed to run in
real-time and is intensively evaluated in simulation and real world
experiments. The results compellingly demonstrate that our proposed algorithm
consistently navigates uneven terrains with high efficiency and surpasses the
performance of other planners. The code and video can be found here:
https://rb.gy/3ov2r8
