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 large-scale indoor environments and based on published, standard datasets illustrate the advantages of our methods over previous Rao-Blackwellized mapping approaches. © 2006 IEEE.
Speeding-up Rao-Blackwellized SLAM / Grisetti, Giorgio; G. D., Tipaldi; C., Stachniss; W., Burgard; Nardi, Daniele. - 2006:(2006), pp. 442-447. (Intervento presentato al convegno 2006 IEEE International Conference on Robotics and Automation, ICRA 2006 tenutosi a Orlando, FL nel 15 May 2006 through 19 May 2006) [10.1109/robot.2006.1641751].
Speeding-up Rao-Blackwellized SLAM
GRISETTI, GIORGIO;NARDI, Daniele
2006
Abstract
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 large-scale indoor environments and based on published, standard datasets illustrate the advantages of our methods over previous Rao-Blackwellized mapping approaches. © 2006 IEEE.File | Dimensione | Formato | |
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