Speeding-up rao-blackwellized SLAM

摘要

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