Sequential 3D-SLAM for mobile action planning

Reliable mapping and self-localization in three dimensions while moving is essential to survey inaccessible work spaces or to inspect technical plants autonomously. Our solution to this 3D SLAM problem is novel in several respects. First, a new rotating laser-scanning setup is presented for acquiring point clouds and reducing them to surface patches in real time. Second, the SLAM algorithms work entirely on highly reduced, attributed surface models and in 3D. Third, we propose a novel system architecture of an extended Kalman filter (EKF) for 3D position tracking, cooperating with a 3D range image understanding system for matching, aligning, and integrating overlapping range views. The system is demonstrated by an indoor exploration tour.