Visually Mapping the RMS Titanic: Conservative Covariance Estimates for SLAM Information Filters
International Journal of Robotics Research
Exactly Sparse Extended Information Filters for Feature-based SLAM
International Journal of Robotics Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Thin junction tree filters for simultaneous localization and mapping
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
| Hi-index | 0.00 |
The Simultaneous Localization and Mapping problem is a fundamental problem in mobile robotics: while a robot navigates in an unknown environment, it must incrementally build a map of its surroundings and localize itself within that map. Traditional approaches to the problem are based upon Kalman filters, but suffer from complexity issues: first, the belief state grows quadratically in the size of the map; and second, the filtering operation can take time quadratic in the size of the map. I present a linear-space filter that maintains a tractable approximation of the belief state as a thin junction tree. The junction tree grows under measurement and motion updates and is periodically "thinned" to remain tractable. The time complexity of the filter operation is linear in the size of the map. I also present simple enhancements that permit constant-time approximate filtering.