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 \textbf{$12.40\%$} against the traditional navigation method. We share our findings on our project webpage (see https://raaslab.org/projects/ProxMaP ).

翻译：在典型的地面机器人路径规划流程中，机器人会随着移动逐步构建环境地图（例如占据网格图）。在室内导航时，由于遮挡，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未探明的区域，而保守的规划器需要规避这些区域。反之，若机器人能准确预测其周围及被遮挡区域的状态，则导航效率可得到提升。本研究聚焦于机器人可到达距离内的占据情况预测，以实现更快速的导航，并提出了一种名为ProxMaP的自监督近端占据地图预测方法。实验表明，ProxMaP能在真实场景与仿真域之间良好泛化，并在仿真环境中将机器人导航效率较传统方法提升 \textbf{$12.40\%$}。相关成果详见项目网站（https://raaslab.org/projects/ProxMaP）。