Multi-robot SLAM with Sparse Extended Information Filers
We present an algorithm for the multi-robot simultaneous localization and mapping (SLAM) problem. Our algorithm enables teams of robots to build joint maps, even if their relative starting locations are unknown and landmarks are ambiguous—which is presently an open problem in robotics. It achieves this capability through a sparse information filter technique, which represents maps and robot poses by Gaussian Markov random fields. The alignment of local maps into a single global maps is achieved by a tree-based algorithm for searching similar-looking local landmark configurations, paired with a hill climbing algorithm that maximizes the overall likelihood by search in the space of correspondences. We report favorable results obtained with a real-world benchmark data set.