Legged navigation is typically examined within open-world, off-road, and challenging environments. In these scenarios, estimating external disturbances requires a complex synthesis of multi-modal information. This underlines a major limitation in existing works that primarily focus on avoiding obstacles. In this work, we propose TOP-Nav, a novel legged navigation framework that integrates a comprehensive path planner with Terrain awareness, Obstacle avoidance and close-loop Proprioception. TOP-Nav underscores the synergies between vision and proprioception in both path and motion planning. Within the path planner, we present and integrate a terrain estimator that enables the robot to select waypoints on terrains with higher traversability while effectively avoiding obstacles. In the motion planning level, we not only implement a locomotion controller to track the navigation commands, but also construct a proprioception advisor to provide motion evaluations for the path planner. Based on the close-loop motion feedback, we make online corrections for the vision-based terrain and obstacle estimations. Consequently, TOP-Nav achieves open-world navigation that the robot can handle terrains or disturbances beyond the distribution of prior knowledge and overcomes constraints imposed by visual conditions. Building upon extensive experiments conducted in both simulation and real-world environments, TOP-Nav demonstrates superior performance in open-world navigation compared to existing methods.

翻译：足式导航通常在开放世界、非道路及复杂环境中进行研究。在此类场景中，对外部干扰的估计需要多模态信息的复杂综合。这突显了现有研究主要聚焦于避障的重大局限性。本文提出TOP-Nav，一种全新的足式导航框架，该框架集成了一种融合地形感知、障碍物规避与闭环本体感知的综合路径规划器。TOP-Nav强调了视觉与本体感知在路径规划与运动规划中的协同作用。在路径规划器中，我们提出并集成了一种地形估计器，使机器人能够选择可通行性更高的地形上的航路点，同时有效避开障碍物。在运动规划层面，我们不仅实现了追踪导航指令的运动控制器，还构建了一个为路径规划器提供运动评估的本体感知顾问。基于闭环运动反馈，我们对基于视觉的地形与障碍物估计进行在线修正。因此，TOP-Nav实现了开放世界导航，使机器人能够处理超出先验知识分布范围的地形或干扰，并克服视觉条件带来的限制。基于在仿真与真实环境中开展的大量实验，TOP-Nav在开放世界导航中展现出优于现有方法的性能。