A Markov Chain Monte Carlo Approach to Closing the Loop in SLAM

The problem of simultaneous localization and mapping has received much attention over the last years. Especially large scale environments, where the robot trajectory loops back on itself, are a challenge. In this paper we introduce a new solution to this problem of closing the loop. Our algorithm is EM-based, but differs from previous work. The key is a probability distribution over partitions of feature tracks that is determined in the E-step, based on the current estimate of the motion. This virtual structure is then used in the M-step to obtain a better estimate for the motion. We demonstrate the success of our algorithm in experiments on real laser data.