In a typical path planning pipeline for a ground robot, we build a map (e.g., an occupancy grid) of the environment as the robot moves around. While navigating indoors, a ground robot's knowledge about the environment may be limited due to occlusions. Therefore, the map will have many as-yet-unknown regions that may need to be avoided by a conservative planner. Instead, if a robot is able to correctly predict what its surroundings and occluded regions look like, the robot may be more efficient in navigation. In this work, we focus on predicting occupancy within the reachable distance of the robot to enable faster navigation and present a self-supervised proximity occupancy map prediction method, named ProxMaP. We show that ProxMaP generalizes well across realistic and real domains, and improves the robot navigation efficiency in simulation by 12.40% against the traditional navigation method. We share our findings on our project webpage (see http://raaslab.org/projects/ProxMaP/ ).

翻译：在典型的地面机器人路径规划流程中，随着机器人移动，我们会构建环境地图（如占据栅格图）。在室内导航时，由于遮挡物存在，地面机器人对环境的认知可能受限。因此，地图中会存在大量尚不可知区域，保守规划器可能需要避开这些区域。相反，若机器人能够正确预测其周围及遮挡区域的环境状态，则导航效率可能得以提升。本文聚焦于在机器人可达距离内预测占据状态以实现更快速导航，并提出一种名为ProxMaP的自监督近端占据图预测方法。实验表明，ProxMaP在仿真域与现实域中均表现出良好泛化性，相比传统导航方法，其在仿真环境中的机器人导航效率提升了12.40%。相关发现已发布于项目网页（见http://raaslab.org/projects/ProxMaP/）。