Factor Graphs, Bayes Trees and Preconditioning for SLAM and SFM

Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SFM) are important and closely related problems in robotics and vision. I will review how SLAM and SFM can be posed in terms of factor graphs, and that inference in these domains can be understood as variable elimination. I will then present the Bayes tree as a novel data structure for representing the inferred posteriors, and show how the Bayes tree can be updated incrementally, yielding an efficient, just-in-time algorithm (which we call iSAM 2). Finally, I will talk about the challenges of using these methods in graphs with dense cliques in them, and show how identifying an efficient sub-problem (subgraph) can yield pre-conditioners for iterative methods to attack truly large-scale problems.

Frank Dellaert is an Associate Professor in the School of Interactive Computing, College of Computing at Georgia Tech. His research is in the areas of Robotics and Computer vision. He is particularly interested in graphical model techniques to solve large-scale problems in mapping and 3D reconstruction. You can find out about his research and publications at http://www.cc.gatech.edu/~dellaert