Visually Mapping the RMS Titanic: Conservative Covariance Estimates for SLAM Information Filters
dc.contributor.author | Eustice, Ryan M. | en_US |
dc.contributor.author | Singh, Hanumant | en_US |
dc.contributor.author | Leonard, John J. | en_US |
dc.contributor.author | Walter, Matthew R. | en_US |
dc.date.accessioned | 2011-08-18T18:24:39Z | |
dc.date.available | 2011-08-18T18:24:39Z | |
dc.date.issued | 2006-12 | en_US |
dc.identifier.citation | Eustice, R. M.; Singh, H.; Leonard, J. J.; Walter, M. R. (2006). "Visually Mapping the RMS Titanic: Conservative Covariance Estimates for SLAM Information Filters." International Journal of Robotics Research 25(12): 1223-1242. <http://hdl.handle.net/2027.42/86056> | en_US |
dc.identifier.issn | 0278-3649 1741-3176 (online) | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/86056 | |
dc.description.abstract | This paper describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of underwater vehicles while exploiting the inertial sensor information that is routinely available on such platforms. We present a novel strategy for efficiently accessing and maintaining consistent covariance bounds within a SLAM information filter, thereby greatly increasing the reliability of data association. The technique is based upon solving a sparse system of linear equations coupled with the application of constant-time Kalman updates. The method is shown to produce consistent covariance estimates suitable for robot planning and data association. Real-world results are reported for a vision-based, six degree of freedom SLAM implementation using data from a recent survey of the wreck of the RMS Titanic. | en_US |
dc.publisher | SAGE | en_US |
dc.title | Visually Mapping the RMS Titanic: Conservative Covariance Estimates for SLAM Information Filters | 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 | Department of Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA 02543 USA. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139 USA. | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/86056/1/reustice-24.pdf | |
dc.identifier.doi | 10.1177/0278364906072512 | en_US |
dc.identifier.source | International Journal of Robotics Research | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
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