Here is the PDF of the talk.

Abstract: Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SFM) are important and closely related problems in robotics and vision. I 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 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 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.