/Loopy SAM

Loopy SAM

Ananth Ranganathan, Michael Kaess and Frank Dellaert
Conference Paper, Intl. Joint Conf. on Artificial Intelligence, IJCAI, Oral presentation acceptance ratio 15.7% (212 of 1353), pp. 2191-2196, January, 2007

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Smoothing approaches to the Simultaneous Local- ization and Mapping (SLAM) problem in robotics are superior to the more common filtering ap- proaches in being exact, better equipped to deal with non-linearities, and computing the entire robot trajectory. However, while filtering algorithms that perform map updates in constant time exist, no analogous smoothing method is available. We aim to rectify this situation by presenting a smoothing- based solution to SLAM using Loopy Belief Prop- agation (LBP) that can perform the trajectory and map updates in constant time except when a loop is closed in the environment. The SLAM prob- lem is represented as a Gaussian Markov Ran- dom Field (GMRF) over which LBP is performed. We prove that LBP, in this case, is equivalent to Gauss-Seidel relaxation of a linear system. The in- ability to compute marginal covariances efficiently in a smoothing algorithm has previously been a stumbling block to their widespread use. LBP enables the efficient recovery of the marginal co- variances, albeit approximately, of landmarks and poses. While the final covariances are overconfi- dent, the ones obtained from a spanning tree of the GMRF are conservative, making them useful for data association. Experiments in simulation and us- ing real data are presented.

BibTeX Reference
author = {Ananth Ranganathan and Michael Kaess and Frank Dellaert},
title = {Loopy SAM},
booktitle = {Intl. Joint Conf. on Artificial Intelligence, IJCAI, Oral presentation acceptance ratio 15.7% (212 of 1353)},
year = {2007},
month = {January},
pages = {2191-2196},