Bayesian Perception

Olivier Aycard

LIG Laboratory - Grenoble University - INRIA Grenoble, France

L. Enrique Sucar

Robotics Laboratory - Department of Computer Science - INAOE, Mexico

The goal of this lecture is to give an overview of Bayesian techniques and their applications to perception for mobile robots and intelligent vehicles. After a brief review of the fundamentals of probability and graph theory, we introduce the different Bayesian models & algorithms generally used in perception, including: Bayesian Classifiers, Bayesian Networks, Hidden Markov Models, Kalman Filters, and Particle Filters. In the second part we present some applications of these models & algorithms for perception solutions such as: gesture recognition, multi-object tracking, intelligent vehicles and medical applications.