论文标题
Proxmap：高效室内机器人导航的近端占用图预测
ProxMaP: Proximal Occupancy Map Prediction for Efficient Indoor Robot Navigation
论文作者
论文摘要
计划移动机器人的路径通常需要在机器人移动时构建环境的地图（例如，占用网格）。在未知环境中导航时，机器人在线构建的地图可能有许多尚未尚未尚未尚未尚未尚不尚未尚未尚未尚未尚未进行。保守的计划者可能会避免此类地区需要更长的时间才能实现目标。相反，如果机器人能够正确预测被遮挡区域的占用率，则机器人可以有效地导航。我们提出了一种自制的占用预测技术，即Proxmap，以预测机器人靠近的占用率，以实现更快的导航。我们表明，Proxmap在现实和真实的域中很好地概括了，并通过传统的导航方法提高了仿真的机器人导航效率12.40％。我们在https://raaslab.org/projects/proxmap上分享我们的发现和代码。
Planning a path for a mobile robot typically requires building a map (e.g., an occupancy grid) of the environment as the robot moves around. While navigating in an unknown environment, the map built by the robot online may have many as-yet-unknown regions. A conservative planner may avoid such regions taking a longer time to navigate to the goal. Instead, if a robot is able to correctly predict the occupancy in the occluded regions, the robot may navigate efficiently. We present a self-supervised occupancy prediction technique, ProxMaP, to predict the occupancy within the proximity of the robot to enable faster navigation. We show that ProxMaP generalizes well across realistic and real domains, and improves the robot navigation efficiency in simulation by 12.40% against a traditional navigation method. We share our findings and code at https://raaslab.org/projects/ProxMaP.
