A Sample of Monte Carlo Methods in Robotics and Vision

Abstract

Approximate inference by sampling from an appropriately constructed posterior has recently seen a dramatic increase in popularity in both the robotics and computer vision community. In this paper, I will describe a number of approaches in which my co-authors and I have used Sequential Monte Carlo methods and Markov chain Monte Carlo sampling to solve a variety of difficult and challenging inference problems. Very recently, we have also used sampling over variable dimension state spaces to perform automatic model selection. I will present two examples of this, one in the domain of computer vision, the other in mobile robotics. In both cases Rao-Blackwellization was used to integrate out the variable dimension-part of the state space, and hence the sampling was done purely over the (combinatorially large) space of different models. This paper describes joint work with many collaborators over the past 5 years, both at Carnegie Mellon University and at the Georgia Institute of Technology, including Dieter Fox, Sebastian Thrun, Wolfram Burgard, Zia Khan, Tucker Balch, Michael Kaess, Rafal Zboinski, and Ananth Ranganathan.