  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.
