A hierarchical approach to the Multi-Vehicle SLAM problem
In this paper we present a novel hierarchical solution to the Multi-Vehicle SLAM (MVSLAM) problem by extending the recently developed random finite set (RFS) based SLAM filter framework. Instead of fusing control and measurement data at each time step, we introduce a RFS Single-Vehicle SLAM based su...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/11509 |
| _version_ | 1848747825139023872 |
|---|---|
| author | Moratuwage, D. Vo, Ba-Ngu Wang, D. |
| author2 | Gee Wah NG |
| author_facet | Gee Wah NG Moratuwage, D. Vo, Ba-Ngu Wang, D. |
| author_sort | Moratuwage, D. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper we present a novel hierarchical solution to the Multi-Vehicle SLAM (MVSLAM) problem by extending the recently developed random finite set (RFS) based SLAM filter framework. Instead of fusing control and measurement data at each time step, we introduce a RFS Single-Vehicle SLAM based sub-mapping process, where each robot periodically produces a local sub-map of its vicinity and communicates the resultant sub-map along with the sequence of applied control commands for further fusion into a higher level MVSLAM algorithm, reducing the required network bandwidth and computational power at the fusion node. Our solution is based on the factorization of MVSLAM posterior into a product of the vehicle trajectories posterior and the landmark map posterior conditioned on the vehicle trajectory. The landmarks and the measurements are modelled as RFSs, instead of using data from exteroceptive sensors, measurements are derived from the produced local sub-maps. The vehicle trajectories posterior is estimated using a Rao-Blackwellised particle filter, while the landmark map posterior is estimated using a Gaussian mixture (GM) probability hypothesis density (PHD) filter. |
| first_indexed | 2025-11-14T06:55:18Z |
| format | Conference Paper |
| id | curtin-20.500.11937-11509 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:55:18Z |
| publishDate | 2012 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-115092017-01-30T11:25:08Z A hierarchical approach to the Multi-Vehicle SLAM problem Moratuwage, D. Vo, Ba-Ngu Wang, D. Gee Wah NG In this paper we present a novel hierarchical solution to the Multi-Vehicle SLAM (MVSLAM) problem by extending the recently developed random finite set (RFS) based SLAM filter framework. Instead of fusing control and measurement data at each time step, we introduce a RFS Single-Vehicle SLAM based sub-mapping process, where each robot periodically produces a local sub-map of its vicinity and communicates the resultant sub-map along with the sequence of applied control commands for further fusion into a higher level MVSLAM algorithm, reducing the required network bandwidth and computational power at the fusion node. Our solution is based on the factorization of MVSLAM posterior into a product of the vehicle trajectories posterior and the landmark map posterior conditioned on the vehicle trajectory. The landmarks and the measurements are modelled as RFSs, instead of using data from exteroceptive sensors, measurements are derived from the produced local sub-maps. The vehicle trajectories posterior is estimated using a Rao-Blackwellised particle filter, while the landmark map posterior is estimated using a Gaussian mixture (GM) probability hypothesis density (PHD) filter. 2012 Conference Paper http://hdl.handle.net/20.500.11937/11509 IEEE restricted |
| spellingShingle | Moratuwage, D. Vo, Ba-Ngu Wang, D. A hierarchical approach to the Multi-Vehicle SLAM problem |
| title | A hierarchical approach to the Multi-Vehicle SLAM problem |
| title_full | A hierarchical approach to the Multi-Vehicle SLAM problem |
| title_fullStr | A hierarchical approach to the Multi-Vehicle SLAM problem |
| title_full_unstemmed | A hierarchical approach to the Multi-Vehicle SLAM problem |
| title_short | A hierarchical approach to the Multi-Vehicle SLAM problem |
| title_sort | hierarchical approach to the multi-vehicle slam problem |
| url | http://hdl.handle.net/20.500.11937/11509 |