Stereo-Vision Based Obstacle Mapping for Indoor/Outdoor SLAM

Abstract

The creation of local and global maps is crucial for (semi-)autonomous operation of mobile robots in previously unknown environments, e.g. during search and rescue missions. We developed an on-board stereo-vision based mapping system, thereby introducing local obstacle maps that can directly be used for fast local obstacle avoidance and path planning. In addition, we designed them to constitute a suitable input to a widely-used simultaneous localization and mapping (SLAM) algorithm. We performed experiments in unknown indoor, unstructured outdoor as well as mixed environments and demonstrated the applicability of our method to camera setups with small as well as wide field of view. In all three scenarios, we achieved a final 2D position error of less than 0.08% of the full trajectory.