PeRL STUDIES AUTONOMOUS NAVIGATION & MAPPING FOR MOBILE ROBOTS IN A PRIORI UNKNOWN ENVIRONMENTS.
Exactly sparse delayed-state filters
Summary
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.
Bibtex entry
@INPROCEEDINGS { reustice-2005a,
AUTHOR = { Ryan M. Eustice and Hanumant Singh and John J. Leonard },
TITLE = { Exactly sparse delayed-state filters },
BOOKTITLE = { Proceedings of the IEEE International Conference on Robotics and Automation },
YEAR = { 2005 },
MONTH = { April },
ADDRESS = { Barcelona, Spain },
PAGES = { 2417--2424 },
}