PeRL STUDIES AUTONOMOUS NAVIGATION & MAPPING FOR MOBILE ROBOTS IN A PRIORI UNKNOWN ENVIRONMENTS.
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Long-term simultaneous localization and mapping with generic linear constraint node removal
Summary
Abstract
This paper reports on the use of generic linear constraint (GLC) node removal as a method to control the computational complexity of long-term simultaneous localization and mapping. We experimentally demonstrate that GLC provides a principled and flexible tool enabling a wide variety of complexity management schemes. Specifically, we consider two main classes: batch multi-session node removal, in which nodes are removed in a batch operation between mapping sessions, and online node removal, in which nodes are removed as the robot operates. Results are shown for 34.9 h of real-world indoor-outdoor data covering 147.4 km collected over 27 mapping sessions spanning a period of 15 months.
Bibtex entry
@INPROCEEDINGS { ncarlevaris-2013b,
AUTHOR = { Nicholas Carlevaris-Bianco and Ryan M. Eustice },
TITLE = { Long-term simultaneous localization and mapping with generic linear constraint node removal },
BOOKTITLE = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems },
YEAR = { 2013 },
MONTH = { November },
ADDRESS = { Tokyo, Japan },
PAGES = { 1034--1041 },
DOI = { 10.1109/IROS.2013.6696478 },
}