Extensions with RFSs in SLAM
This book demonstrates that the inherent uncertainty of feature maps and feature map measurements can be naturally encapsulated by random finite set models, and subsequently in Chapter 5 proposed the multi-feature RFSSLAM framework and recursion of equations 5.5 and 5.6. The SLAM solutions presented...
| Main Authors: | , , , |
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| Format: | Book Chapter |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/61599 |
| _version_ | 1848760697696026624 |
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| author | Mullane, J. Vo, Ba-Ngu Adams, M. Vo, B. |
| author_facet | Mullane, J. Vo, Ba-Ngu Adams, M. Vo, B. |
| author_sort | Mullane, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This book demonstrates that the inherent uncertainty of feature maps and feature map measurements can be naturally encapsulated by random finite set models, and subsequently in Chapter 5 proposed the multi-feature RFSSLAM framework and recursion of equations 5.5 and 5.6. The SLAM solutions presented thus far focussed on the joint propagation of the the first-order statistical moment or expectation of the RFS map, i.e. its Probability Hypothesis Density, v k , and the vehicle trajectory. Recall from Chapter 3 that the integral of the PHD, which operates on a feature state space, gives the expected number of features in the map, at its maxima represent regions in Euclidean map space where features are most likely to exist. |
| first_indexed | 2025-11-14T10:19:54Z |
| format | Book Chapter |
| id | curtin-20.500.11937-61599 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:19:54Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-615992018-06-18T01:06:43Z Extensions with RFSs in SLAM Mullane, J. Vo, Ba-Ngu Adams, M. Vo, B. This book demonstrates that the inherent uncertainty of feature maps and feature map measurements can be naturally encapsulated by random finite set models, and subsequently in Chapter 5 proposed the multi-feature RFSSLAM framework and recursion of equations 5.5 and 5.6. The SLAM solutions presented thus far focussed on the joint propagation of the the first-order statistical moment or expectation of the RFS map, i.e. its Probability Hypothesis Density, v k , and the vehicle trajectory. Recall from Chapter 3 that the integral of the PHD, which operates on a feature state space, gives the expected number of features in the map, at its maxima represent regions in Euclidean map space where features are most likely to exist. 2011 Book Chapter http://hdl.handle.net/20.500.11937/61599 10.1007/978-3-642-21390-8_7 restricted |
| spellingShingle | Mullane, J. Vo, Ba-Ngu Adams, M. Vo, B. Extensions with RFSs in SLAM |
| title | Extensions with RFSs in SLAM |
| title_full | Extensions with RFSs in SLAM |
| title_fullStr | Extensions with RFSs in SLAM |
| title_full_unstemmed | Extensions with RFSs in SLAM |
| title_short | Extensions with RFSs in SLAM |
| title_sort | extensions with rfss in slam |
| url | http://hdl.handle.net/20.500.11937/61599 |