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

Michael Kaess and Frank Dellaert
Conference Paper, IEEE Intl. Conf. on Robotics and Automation, ICRA, pp. 645-650, April, 2005

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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.

author = {Michael Kaess and Frank Dellaert},
title = {A Markov Chain Monte Carlo Approach to Closing the Loop in SLAM},
booktitle = {IEEE Intl. Conf. on Robotics and Automation, ICRA},
year = {2005},
month = {April},
pages = {645-650},
} 2017-09-13T10:43:28-04:00