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.

在这项工作中，我们提出了一个新颖的四肢导航框架 TOP-Nav，它将全面路径规划器与地形感知、避障和闭环本体感知整合在一起，突出了视觉和本体感知在路径和动作规划中的协同作用。通过在路径规划器中引入地形估计，我们使机器人能够在更易通过的地形上选择航点，同时有效避开障碍物。在动作规划水平上，我们不仅实现了一种运动控制器来跟踪导航命令，还构建了一个本体感知顾问，为路径规划器提供动作评估。基于闭环运动反馈，我们对基于视觉的地形和障碍物估计进行在线校正。因此，TOP-Nav 在开放世界导航中实现了机器人能够处理先前知识分布范围之外的地形或干扰，并克服了视觉条件造成的约束。通过在模拟和真实环境中进行广泛实验，TOP-Nav 在开放世界导航中展现出了优于现有方法的性能。