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
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;
Journal_Title :
Robotics, IEEE Transactions on
DOI :
10.1109/TRO.2013.2264869