Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks

Particle filtering in high dimensional state-spaces can be inefficient because a large number of samples is needed to represent the posterior. A standard technique to increase the efficiency of sampling techniques is to reduce the size of the state space by marginalizing out some of the variables analytically; this is called Rao-Blackwellisation (Casella and Robert 1996). Combining these two techniques results in Rao-Blackwellised particle filtering (RBPF) (Doucet 1998, Doucet, de Freitas, Murphy and Russell 2000). In this chapter, we explain RBPF, discuss when it can be used, and give a detailed example of its application to the problem of map learning for a mobile robot, which has a very large (~ 2¹⁰⁰) discrete state space.

Editors and Affiliations

1. Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria, 3010, Australia

   Arnaud Doucet

2. Computer Science Division, University of California, 387 Soda Hall, 94720-1776, Berkeley, CA, USA

   Nando de Freitas

3. Pattern and Information Processing, Defence Evaluation and Research Agency, St. Andrews Road, WR14 3PS, Malvern, Worcs, UK

   Neil Gordon

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