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

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Nicholas Carlevaris-Bianco and Ryan M. Eustice, Long-term simultaneous localization and mapping with generic linear constraint node removal. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1034-1041, Tokyo, Japan, November 2013.

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 },
}