PeRL STUDIES AUTONOMOUS NAVIGATION & MAPPING FOR MOBILE ROBOTS IN A PRIORI UNKNOWN ENVIRONMENTS.
Generic factor-based node marginalization and edge sparsification for pose-graph SLAM
Summary
Nicholas Carlevaris-Bianco and Ryan M. Eustice, Generic factor-based node marginalization and edge sparsification for pose-graph SLAM. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 5728-5735, Karlsruhe, Germany, May 2013.Abstract
This paper reports on a factor-based method for node marginalization in simultaneous localization and mapping (SLAM) pose-graphs. Node marginalization in a pose-graph induces fill-in and leads to computational challenges in performing inference. The proposed method is able to produce a new set of constraints over the elimination clique that can represent either the true marginalization, or a sparse approximation of the true marginalization using a Chow-Liu tree. The proposed algorithm improves upon existing methods in two key ways: First, it is not limited to strictly full-state relative-pose constraints and works equally well with other low-rank constraints such as those produced by monocular vision. Second, the new factors are produced in a way that accounts for measurement correlation, a problem ignored in other methods that rely upon measurement composition. We evaluate the proposed method over several real-world SLAM graphs and show that it outperforms other state-of-the-art methods in terms of Kullback-Leibler divergence.
Bibtex entry
@INPROCEEDINGS { ncarlevaris-2013a,
AUTHOR = { Nicholas Carlevaris-Bianco and Ryan M. Eustice },
TITLE = { Generic factor-based node marginalization and edge sparsification for pose-graph {SLAM} },
BOOKTITLE = { Proceedings of the IEEE International Conference on Robotics and Automation },
YEAR = { 2013 },
MONTH = { May },
ADDRESS = { Karlsruhe, Germany },
PAGES = { 5728--5735 },
}