Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling
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- 1 January 2005
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Recently Rao-Blackwellized particle filters have been introduced as effective means to solve the simultaneous localization and mapping (SLAM) problem. This approach uses a particle filter in which each particle carries an individual map of the environment. Accordingly, a key question is how to reduce the number of particles. In this paper we present adaptive techniques to reduce the number of particles in a Rao-Blackwellized particle filter for learning grid maps. We propose an approach to compute an accurate proposal distribution taking into account not only the movement of the robot but also the most recent observation. This drastically decrease the uncertainty about the robot's pose in the prediction step of the filter. Furthermore, we present an approach to selectively carry out re-sampling operations which seriously reduces the problem of particle depletion. Experimental results carried out with mobile robots in large-scale indoor as well as in outdoor environments illustrate the advantages of our methods over previous approachesKeywords
This publication has 8 references indexed in Scilit:
- A Discussion of Simultaneous Localization and MappingAutonomous Robots, 2006
- An efficient fastslam algorithm for generating maps of large-scale cyclic environments from raw laser range measurementsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Simultaneous localization and mapping with unknown data association using FastSLAMPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Map building with mobile robots in populated environmentsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Monte Carlo localization for mobile robotsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- A computationally efficient solution to the simultaneous localisation and map building (SLAM) problemPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A Probabilistic On-Line Mapping Algorithm for Teams of Mobile RobotsThe International Journal of Robotics Research, 2001
- Metropolized independent sampling with comparisons to rejection sampling and importance samplingStatistics and Computing, 1996