Fast and accurate SLAM with Rao-Blackwellized particle filters

Authors:
Giorgio Grisetti;Gian Diego Tipaldi;Cyrill Stachniss;Wolfram Burgard;Daniele Nardi
Affiliations:
University of Freiburg, Department of Computer Science, D-79110 Freiburg, Germany and Dipartimento Informatica e Sistemistica, Universitá "La Sapienza", I-00198 Rome, Italy;Dipartimento Informatica e Sistemistica, Universitá "La Sapienza", I-00198 Rome, Italy;Eidgenössische Technische Hochschule Zurich (ETH), IRIS, 8092 Zurich, Switzerland and University of Freiburg, Department of Computer Science, D-79110 Freiburg, Germany;University of Freiburg, Department of Computer Science, D-79110 Freiburg, Germany;Dipartimento Informatica e Sistemistica, Universitá "La Sapienza", I-00198 Rome, Italy
Venue:
Robotics and Autonomous Systems
Year:
2007

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Abstract

Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. This technique applies a particle filter in which each particle carries an individual map of the environment. Accordingly, a key issue is to reduce the number of particles and/or to make use of compact map representations. This paper presents an approximative but highly efficient approach to mapping with Rao-Blackwellized particle filters. Moreover, it provides a compact map model. A key advantage is that the individual particles can share large parts of the model of the environment. Furthermore, they are able to reuse an already computed proposal distribution. Both techniques substantially speed up the overall filtering process and reduce the memory requirements. Experimental results obtained with mobile robots in large-scale indoor environments and based on published standard datasets illustrate the advantages of our methods over previous mapping approaches using Rao-Blackwellized particle filters.