Speeding-up Rao-blackwellized SLAM

— Recently, 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 re-use an already computed proposal distribution. Both techniques substantially speed up the overall process and reduce the memory requirements. Experimental results obtained with mobile robots in largescale indoor environments and based on published, standard datasets illustrate the advantages of our methods over previous Rao-Blackwellized...

• Type: Conference
• Year: 2006
• Where: ICRA
• Authors: Giorgio Grisetti, Gian Diego Tipaldi, Cyrill Stachniss, Wolfram Burgard, Daniele Nardi
• Keywords: ICRA 2006, Mapping Problem, Particle filters, Rao-Blackwellized Particle filters, Robotics