Speeding-Up Robot Exploration by Exploiting Background Information
Stefan Obwald, Maren Bennewitz, Wolfram Burgard, Cyrill Stachniss · IEEE Robotics and Automation Letters · 2016
The ability to autonomously learn a model of an environment is an important capability of a mobile robot. In this paper, we investigate the problem of exploring a scene given background information in form of a topo-metric graph of the environment. Our method is relevant for several real-world applications in which the rough structure of the environment is known beforehand. We present an approach that exploits such background information and enables a robot to cover the environment with its sensors faster compared to a greedy exploration system without this information. We implemented our exploration system in ROS and evaluated it in different environments. As the experimental results demonstrate, our proposed method significantly reduces the overall trajectory length needed to cover the environment with the robot's sensors and thus yields a more efficient exploration strategy compared to state-of-the-art greedy exploration, if the additional information is available.