Ryan Eustice, Hanumant Singh, and John Leonard, Exactly Sparse Delayed-State Filters, In Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp 2428--2435, April 2005.
Best Student Paper Award.
Abstract
This paper presents the novel insight that the SLAM information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment which rely upon scan-matching raw sensor data. Scan-matching raw data results in virtual observations of robot motion with respect to a place its previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms like Sparse Extended Information Filters or Thin Junction Tree Filters. These methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparseness of the delayed-state framework is that it allows one to take advantage of the information space parameterization without having to make any approximations. Therefore, it can produce equivalent results to the "full-covariance" solution.
@inproceedings{reustice-2005a,
AUTHOR = {Eustice, R. and Singh, H. and Leonard, J.},
YEAR = {2005},
MONTH = {April},
TITLE = {Exactly Sparse Delayed-State Filters},
BOOKTITLE = {Proceedings of the 2005 IEEE International Conference on Robotics and Automation},
ADDRESS = {Barcelona, Spain},
PAGES = {2428--2435},
}
FILE » icra2005.pdf
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