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.

翻译：我们提出AMCO，一种用于四足机器人的新型导航方法，该方法自适应地融合基于视觉与基于本体感知的感知能力。我们的方法利用三种代价地图：通用知识地图、可通行性历史地图和当前本体感知地图，这些地图源自机器人的视觉与本体感知数据，并通过耦合得到用于导航的耦合可通行性代价地图。通用知识地图编码从视觉感知中语义分割得到的地形信息，表示地形典型预期可通行性；可通行性历史地图将机器人近期在地形上的本体感知测量值及其语义分割编码为代价地图；当前本体感知地图则将机器人当前的本体感知测量值编码为代价地图。由于通用知识地图和可通行性历史地图依赖语义分割，我们通过估计输入RGB图像的亮度和运动模糊来评估视觉传感数据的可靠性，并相应融合三种代价地图以生成用于导航的耦合可通行性代价地图。借助这种自适应耦合，机器人能够依赖当前最可靠的输入模态。最后，我们提出一种新型规划器，利用耦合可通行性代价地图为穿越复杂户外环境选择合适的步态和速度。我们在不同真实户外环境中验证了AMCO的导航性能，与现有导航方法相比，两项稳定性指标降低10.8%-34.9%，成功率提升最高达50%。