Exactly Sparse Delayed-State Filters for View-Based SLAM
dc.contributor.author | Eustice, Ryan M. | en_US |
dc.contributor.author | Singh, Hanumant | en_US |
dc.contributor.author | Leonard, John J. | en_US |
dc.date.accessioned | 2011-08-18T18:24:40Z | |
dc.date.available | 2011-08-18T18:24:40Z | |
dc.date.issued | 2006-12 | en_US |
dc.identifier.citation | Eustice; R. M.; Singh, H.; Leonard, J. J. (2006). "Exactly Sparse Delayed-State Filters for View-Based SLAM." IEEE Transactions on Robotics 22(6):1100-1114. <http://hdl.handle.net/2027.42/86062> | en_US |
dc.identifier.issn | 1552-3098; 1042-296X; 1546-1904. | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/86062 | |
dc.description.abstract | This paper reports the novel insight that the simultaneous localization and mapping (SLAM) information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment that rely upon scan-matching raw sensor data to obtain virtual observations of robot motion with respect to a place it has previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms, such as sparse extended information filter or thin junction-tree filter, since these methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparsity of the delayed-state framework is that it allows one to take advantage of the information space parameterization without incurring any sparse approximation error. Therefore, it can produce equivalent results to the full-covariance solution. The approach is validated experimentally using monocular imagery for two datasets: a test-tank experiment with ground truth, and a remotely operated vehicle survey of the RMS Titanic. | en_US |
dc.publisher | IEEE | en_US |
dc.title | Exactly Sparse Delayed-State Filters for View-Based SLAM | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Naval Architecture and Marine Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationother | Joint Program in Oceanogr. Eng., Massachusetts Inst. of Technol., Cambridge, MA | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/86062/1/reustice-25.pdf | |
dc.identifier.doi | 10.1109/TRO.2006.886264 | en_US |
dc.identifier.source | IEEE Transactions on Robotics | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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