Date of Original Version
Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on, pp.4943-4948, 10-15 Oct. 2009
Abstract or Table of Contents
We present a method for topological SLAM that specifically targets loop closing for edge-ordered graphs. Instead of using a heuristic approach to accept or reject loop closing, we propose a probabilistically grounded multi-hypothesis technique that relies on the incremental construction of a map/state hypothesis tree. Loop closing is introduced automatically within the tree expansion, and likely hypotheses are chosen based on their posterior probability after a sequence of sensor measurements. Careful pruning of the hypothesis tree keeps the growing number of hypotheses under control and a recursive formulation reduces storage and computational costs. Experiments are used to validate the approach.