Exactly sparse delayed-state filters for view-based SLAM
Eustice, Ryan M.
Leonard, John J.
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SubjectInformation filters; Kalman filtering; Machine vision; Mobile robot motion planning; Mobile robots; Recursive estimation; Robot vision systems; Simultaneous localization and mapping; Underwater vehicles
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.
Author Posting. © IEEE, 2006. This article is posted here by permission of IEEE for personal use, not for redistribution. The definitive version was published in IEEE Transactions on Robotics 22 (2006): 1110-1114, doi:10.1109/TRO.2006.886264.
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