R.M. Eustice, H. Singh, J.J. Leonard, Exactly Sparse Delayed-State Filters for View-Based SLAM, IEEE Transaction on Robotics, vol. 22, no. 6, pp. 1100-1114, Dec. 2006
2006 King-Sun Fu Memorial Best Transactions on Robotics Paper Award of the
IEEE Robotics and Automation Society.
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.
@ARTICLE{reustice-2006b,
author = {Eustice, R. M. and Singh, H. and Leonard, J. J.},
title = {Exactly sparse delayed-state filters for view-based {SLAM}},
journal = {IEEE Trans. Robotics},
year = {2006},
volume = {22},
pages = {1100--1114},
number = {6},
month = dec,
keywords = {SLAM, recursive estimation, mobile robots, mobile robot motion-planning, machine vision, computer vision, robot vision systems, underwater vehicles, Kalman filtering, information filters},
}
FILE » reustice-2006b.pdf
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