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

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/38412

Title: A Markov Chain Monte Carlo Approach to Closing the Loop in SLAM
Author: Kaess, Michael ; Dellaert, Frank
Abstract: 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.
Description: ©2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Presented at the 2005 IEEE International Conference on Robotics and Automation (ICRA), 18-22 April 2005, Barcelona, Spain. DOI: 10.1109/ROBOT.2005.1570190
Type: Post-print
Proceedings
URI: http://hdl.handle.net/1853/38412
ISSN: 1050-4729
Citation: Kaess, M., & Dellaert, F. (2005). “A Markov Chain Monte Carlo Approach to Closing the Loop in SLAM”. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2005), 18-22 April 2005, 643-648.
Date: 2005-04
Contributor: Georgia Institute of Technology. Center for Robotics and Intelligent Machines
Georgia Institute of Technology. College of Computing
Publisher: Georgia Institute of Technology
Institute of Electrical and Electronics Engineers
Subject: Expectation maximization
Loop closing
Probability distribution
Simultaneous localization and mapping

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