A Sample of Monte Carlo Methods in Robotics and Vision


May 27, 2004


Frank Dellaert


College of Computing, Georgia Institute of Technology


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. I will describe a number of approaches in which we 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.


Frank Dellaert

Frank Dellaert is an Assistant Professor at the College of Computing, Georgia Institute of Technology. He graduated in 2001 with a Ph.D. from Carnegie Mellon University. His research focuses on probabilistic methods in Robotics and Computer Vision: he has applied Markov chain Monte Carlo sampling methodologies in a variety of novel settings, most notably to address the correspondence problem in computer vision. Before that, with Dieter Fox and Sebastian Thrun, he has introduced the Monte Carlo localization method for estimating and tracking the pose of robots, which is now a standard and popular tool in mobile robotics. Since coming to Georgia Tech, he explored the theme of probabilistic, model-based reasoning paired with randomized approximation methods in three main research areas: Advanced sequential Monte Carlo methods, Spatio-Temporal Reconstruction from Images, and Simultaneous Localization and Mapping. His homepage can be found at http://www.cc.gatech.edu/~dellaert