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.

翻译：在不平等地形的自主导航中实现高效行进仍是一项具有挑战性的研究。我们提出一种新的基于几何的不平等地形无地图导航框架，该框架结合了稀疏高斯过程（SGP）局部地图与快速探索随机树*（RRT*）规划器。该方法首先生成高分辨率SGP局部地图，为机器人周围环境提供插值表示。该地图捕捉了关键环境变化特征，包括高度、不确定性及坡度特性。随后，我们基于SGP表示构建可通行性地图以引导规划过程。RRT*规划器可高效生成实时导航路径，避开不可通行地形并趋向目标。这种基于SGP的地形解析与RRT*规划相结合的方法，使地面机器人能够在不同海拔和陡峭障碍物环境中安全导航。通过鲁棒的仿真测试评估了所提方法的性能，结果表明相较于现有方法，该方法在实现安全高效导航方面具有显著优势。