Improving indoor navigation of autonomous robots by an explicit representation of doors

In the last decades, tremendous progress has been made in the field of autonomous indoor navigation for mobile robots. However, these approaches assume the structural part of the environment to be completely static. In practice, movable parts of scenes, e.g. doors, frequently violate this assumption which leads to poor performance. Also, mobile manipulation capabilities can only be utilized, if the robot knows about the movability of objects. In this paper, we address an important part of these problems by the explicit representation of doors as door leaves and joints. We propose to augment standard approaches to navigation like 2D occupancy grid mapping and Monte-Carlo-Localization. Our algorithm detects doors during mapping and represents their movability adequately in the map. During localization, the state of doors is estimated from measurements while it is simultaneously used to improve localization robustness and accuracy. In experimental results we demonstrate superior performance of our method compared to a state-of-the-art approach to localization.