Extending Bayesian RFS SLAM to multi-vehicle SLAM
In this paper we present a novel solution to the Multi-Vehicle SLAM (MVSLAM) problem by extending the random finite set (RFS) based SLAM filter framework using two recently developed multi-sensor information fusion approaches. Our solution is based on the modelling of the measurements and the landma...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/10263 |
| _version_ | 1848746183978123264 |
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| author | Moratuwage, D. Vo, Ba-Ngu Wang, D. Wang, H. |
| author_facet | Moratuwage, D. Vo, Ba-Ngu Wang, D. Wang, H. |
| author_sort | Moratuwage, D. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper we present a novel solution to the Multi-Vehicle SLAM (MVSLAM) problem by extending the random finite set (RFS) based SLAM filter framework using two recently developed multi-sensor information fusion approaches. Our solution is based on the modelling of the measurements and the landmark map as RFSs and factorizing the MVSLAM posterior into a product of the joint vehicle trajectories posterior and the landmark map posterior conditioned the vehicle trajectories. The joint vehicle trajectories posterior is propagated using a particle filter while the landmark map posterior conditioned on the vehicle trajectories is propagated using a Gaussian Mixture (GM) implementation of the probability hypothesis density (PHD) filter. © 2012 IEEE. |
| first_indexed | 2025-11-14T06:29:13Z |
| format | Conference Paper |
| id | curtin-20.500.11937-10263 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:29:13Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-102632017-09-13T14:50:54Z Extending Bayesian RFS SLAM to multi-vehicle SLAM Moratuwage, D. Vo, Ba-Ngu Wang, D. Wang, H. In this paper we present a novel solution to the Multi-Vehicle SLAM (MVSLAM) problem by extending the random finite set (RFS) based SLAM filter framework using two recently developed multi-sensor information fusion approaches. Our solution is based on the modelling of the measurements and the landmark map as RFSs and factorizing the MVSLAM posterior into a product of the joint vehicle trajectories posterior and the landmark map posterior conditioned the vehicle trajectories. The joint vehicle trajectories posterior is propagated using a particle filter while the landmark map posterior conditioned on the vehicle trajectories is propagated using a Gaussian Mixture (GM) implementation of the probability hypothesis density (PHD) filter. © 2012 IEEE. 2012 Conference Paper http://hdl.handle.net/20.500.11937/10263 10.1109/ICARCV.2012.6485232 restricted |
| spellingShingle | Moratuwage, D. Vo, Ba-Ngu Wang, D. Wang, H. Extending Bayesian RFS SLAM to multi-vehicle SLAM |
| title | Extending Bayesian RFS SLAM to multi-vehicle SLAM |
| title_full | Extending Bayesian RFS SLAM to multi-vehicle SLAM |
| title_fullStr | Extending Bayesian RFS SLAM to multi-vehicle SLAM |
| title_full_unstemmed | Extending Bayesian RFS SLAM to multi-vehicle SLAM |
| title_short | Extending Bayesian RFS SLAM to multi-vehicle SLAM |
| title_sort | extending bayesian rfs slam to multi-vehicle slam |
| url | http://hdl.handle.net/20.500.11937/10263 |