Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling

Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling. Grisetti, G., Stachniss, C., & Burgard, W. In Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pages 2432-2437, 2005. IEEE.

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Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling [link]

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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 RaoBlackwellized 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 approaches.

@inproceedings{
 title = {Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling},
 type = {inproceedings},
 year = {2005},
 pages = {2432-2437},
 websites = {https://ieeexplore.ieee.org/document/1570477/},
 publisher = {IEEE},
 city = {Barcelona, Spain},
 id = {0bd7f738-a2a1-3353-96e3-5567ff7627f9},
 created = {2022-03-28T09:45:00.802Z},
 accessed = {2022-02-18},
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 profile_id = {235249c2-3ed4-314a-b309-b1ea0330f5d9},
 group_id = {1ff583c0-be37-34fa-9c04-73c69437d354},
 last_modified = {2022-03-29T08:00:56.807Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {grisettiImprovingGridbasedSLAM2005},
 source_type = {inproceedings},
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 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 RaoBlackwellized 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 approaches.},
 bibtype = {inproceedings},
 author = {Grisetti, G and Stachniss, C and Burgard, W},
 doi = {10.1109/ROBOT.2005.1570477},
 booktitle = {Proceedings of the 2005 IEEE International Conference on Robotics and Automation}
}

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