AMCO：面向四足机器人户外环境导航的视觉与本体感受自适应多模态耦合
AMCO: Adaptive Multimodal Coupling of Vision and Proprioception for Quadruped Robot Navigation in Outdoor Environments
AI总结：
提出四足机器人导航方法AMCO，通过自适应耦合视觉与本体感受构建的三种代价地图生成耦合可通行性代价地图，并设计规划器选择步态和速度，在真实户外环境中显著提升了稳定性和成功率。
摘要：
我们提出了 AMCO，一种用于四足机器人的新颖导航方法，能够自适应地结合基于视觉和基于本体感受的感知能力。我们的方法使用三种代价地图：通用知识地图、可通行性历史地图和当前本体感受地图；这些地图源自机器人的视觉和本体感受数据，并将它们耦合以获得用于导航的耦合可通行性代价地图。通用知识地图对从视觉传感中语义分割出的地形进行编码，并代表地形通常预期的可通行性。可通行性历史地图将机器人最近在地形上的本体感受测量及其语义分割编码为代价地图。此外，机器人当前的本体感受测量被编码为当前本体感受地图中的代价地图。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感官数据的可靠性，并据此结合这三种代价地图，获得用于导航的耦合可通行性代价地图。利用这种自适应耦合，机器人可以依赖可用的最可靠输入模态。最后，我们提出了一种新颖的规划器，它使用耦合可通行性代价地图选择适当的步态和速度，以穿越具有挑战性的户外环境。我们在不同的真实世界户外环境中展示了 AMCO 的导航性能，并观察到相较于当前导航方法，两个稳定性指标分别降低了 10.8%-34.9%，成功率提高了多达 50%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
