/The Bayes Tree: Enabling Incremental Reordering and Fluid Relinearization for Online Mapping

The Bayes Tree: Enabling Incremental Reordering and Fluid Relinearization for Online Mapping

Michael Kaess, Viorela Ila, Richard Roberts and Frank Dellaert
Journal Article, Carnegie Mellon University, Computer Science and Artificial Intelligence Laboratory - Technical Report MIT-CSAIL-TR-2010-021, January, 2010

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

In this paper we present a novel data structure, the Bayes tree, which exploits the connections between graphical model inference and sparse linear algebra. The proposed data structure provides a new perspective on an entire class of simultaneous localization and mapping (SLAM) algorithms. Similar to a junction tree, a Bayes tree encodes a factored probability density, but unlike the junction tree it is directed and maps more naturally to the square root information matrix of the SLAM problem. This makes it eminently suited to encode the sparse nature of the problem, especially in a smoothing and mapping (SAM) context. The inherent sparsity of SAM has already been exploited in the literature to produce efficient solutions in both batch and online mapping. The graphical model perspective allows us to develop a novel incremental algorithm that seamlessly incorporates reordering and relinearization. This obviates the need for expensive periodic batch operations from previous approaches, which negatively affect the performance and detract from the intended online nature of the algorithm. The new method is evaluated using simulated and real-world datasets in both landmark and pose SLAM settings.

BibTeX Reference
@article{Kaess-2010-10391,
author = {Michael Kaess and Viorela Ila and Richard Roberts and Frank Dellaert},
title = {The Bayes Tree: Enabling Incremental Reordering and Fluid Relinearization for Online Mapping},
journal = {Computer Science and Artificial Intelligence Laboratory - Technical Report MIT-CSAIL-TR-2010-021},
year = {2010},
month = {January},
}
2017-09-13T10:40:52+00:00