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

翻译：我们提出了一种利用基于稀疏高斯过程（Sparse Gaussian Process, SGP）的局部感知模型，实现崎岖地形自主导航的新方法。该SGP局部感知模型通过局部测距观测（点云）进行训练，以学习地形高程剖面并提取机器人周围可行的导航子目标。随后，我们采用一个优先保障机器人安全性（即确保机器人的横滚角和俯仰角保持在指定范围内）的成本函数，来选择一个安全感知的子目标，从而引导机器人到达最终目的地。该算法设计为实时运行，并在仿真与真实世界实验中进行了深入评估。结果令人信服地表明，我们提出的算法能够高效地持续穿越崎岖地形，且性能优于其他规划器。相关代码与视频可在此处获取：https://rb.gy/3ov2r8