Extensions with RFSs in SLAM
dc.contributor.author | Mullane, J. | |
dc.contributor.author | Vo, Ba-Ngu | |
dc.contributor.author | Adams, M. | |
dc.contributor.author | Vo, B. | |
dc.date.accessioned | 2018-01-30T08:05:52Z | |
dc.date.available | 2018-01-30T08:05:52Z | |
dc.date.created | 2018-01-30T05:59:14Z | |
dc.date.issued | 2011 | |
dc.identifier.citation | Mullane J. and Vo B.N. and Adams M., Vo B.T. (2011) Extensions with RFSs in SLAM, in Random Finite Sets for Robot Mapping and SLAM. Springer Tracts in Advanced Robotics, vol 72, pp. 127-136. Berlin: Springer. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/61599 | |
dc.identifier.doi | 10.1007/978-3-642-21390-8_7 | |
dc.description.abstract |
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. | |
dc.title | Extensions with RFSs in SLAM | |
dc.type | Book Chapter | |
dcterms.source.volume | 72 | |
dcterms.source.startPage | 127 | |
dcterms.source.endPage | 136 | |
dcterms.source.title | Springer Tracts in Advanced Robotics | |
curtin.department | School of Electrical Engineering and Computing | |
curtin.accessStatus | Fulltext not available |
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