DocumentCode :
1760877
Title :
Robust Global Feature Based Data Association With a Sparse Bit Optimized Maximum Clique Algorithm
Author :
San Segundo, Pablo ; Rodriguez-Losada, Diego
Author_Institution :
Center of Autom. & Robot., Univ. Politec. de Madrid, Madrid, Spain
Volume :
29
Issue :
5
fYear :
2013
fDate :
Oct. 2013
Firstpage :
1332
Lastpage :
1339
Abstract :
This paper presents a robust solution to the mobile robotics data association problem based on solving the maximum clique problem (MCP) in a typically sparse correspondence graph, which contains compatibility information between pairs of observations and landmarks. Bit sparse optimizations are designed and implemented in a new algorithm BBMCS, which reduces computation and memory requirements of a leading general purpose maximum clique solver, to make it possibly the best exact sparse MCP algorithm currently found in the literature. BBMCS is reported to achieve very good results in terms of robustness with few assumptions on noise and visibility, while managing very reasonable computation time and memory usage even for complex large data association problems.
Keywords :
graph theory; mobile robots; optimisation; robust control; sensor fusion; storage management; BBMCS; MCP algorithm; complex large data association problems; memory requirements; mobile robotics data association problem; noise assumptions; patibility information; robust global feature-based data association; sparse bit optimized maximum clique algorithm; sparse correspondence graph; visibility assumptions; Combinatorial optimization; computational intelligence; mobile robots; search;
fLanguage :
English
Journal_Title :
Robotics, IEEE Transactions on
Publisher :
ieee
ISSN :
1552-3098
Type :
jour
DOI :
10.1109/TRO.2013.2264869
Filename :
6527958
Link To Document :
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