  We present AMCO, a novel navigation method for quadruped robots that
adaptively combines vision-based and proprioception-based perception
capabilities. Our approach uses three cost maps: general knowledge map;
traversability history map; and current proprioception map; which are derived
from a robot's vision and proprioception data, and couples them to obtain a
coupled traversability cost map for navigation. The general knowledge map
encodes terrains semantically segmented from visual sensing, and represents a
terrain's typically expected traversability. The traversability history map
encodes the robot's recent proprioceptive measurements on a terrain and its
semantic segmentation as a cost map. Further, the robot's present
proprioceptive measurement is encoded as a cost map in the current
proprioception map. As the general knowledge map and traversability history map
rely on semantic segmentation, we evaluate the reliability of the visual
sensory data by estimating the brightness and motion blur of input RGB images
and accordingly combine the three cost maps to obtain the coupled
traversability cost map used for navigation. Leveraging this adaptive coupling,
the robot can depend on the most reliable input modality available. Finally, we
present a novel planner that selects appropriate gaits and velocities for
traversing challenging outdoor environments using the coupled traversability
cost map. We demonstrate AMCO's navigation performance in different real-world
outdoor environments and observe 10.8%-34.9% reduction w.r.t. two stability
metrics, and up to 50% improvement in terms of success rate compared to current
navigation methods.
