A data association approach for SLAM is proposed by combining genetic algorithm and joint maximum likelihood (JML) method, named "GAJML" for short. In that approach, "association door" is used for reducing data association solution space to improve search efficiency. Joint maximum likelihood value of data association solution is taken as fitness value. A self-adaptation strategy is implanted into population initialization to improve computation efficiency. Experimental result indicates that its instantaneity is well with only increasing little time-consumption compared with ICNN (individual compatibility nearest neighbor), the data association correct rate is very high even close to that of the JML method, and can effectively overcome "close loop" problem caused localization accumulated error growing.
[1] Durrant-Whyte H, Bailey T. Simultaneous localization and mapping: Part I[J]. IEEE Robotics & Automation Magazine, 2006, 13(2): 99-107.
[2] Bailey T, Durrant-Whyte H. Simultaneous localization and mapping(SLAM): Part II[J]. IEEE Robotics & Automation Magazine, 2006, 13(2): 108-117.
[3] Hahnel D, Thrun S, Wegbreit B, et al. Towards lazy data association in SLAM[M]//Springer Transactions in Advanced Robotics, vol.15. Berlin, Germany: Springer-Verlag, 2005: 421-431.
[4] 沈林成,徐昕,朱华勇,等.移动机器人自主控制理论与技术[M].北京:科学出版社,2011: 248-270. Shen L C, Xu X, Zhu H Y, et al. Mobile robots independent control theory and technology [M]. Beijing: Science Press, 2011: 248-270.
[5] 周武,赵春霞.SLAM问题的一种优化数据关联算法[J].机器人,2009,31(3): 217-224. Zhou W, Zhao C X. An optimized data association solution for SLAM[J]. Robot, 2009, 31(3): 217-224.
[6] Singer R A, Stein J J. An optimal tracking filter for processing sensor data of imprecisely determined origin in surveillance system[C]//IEEE Conference on Decision and Control. Piscataway, NJ, USA: IEEE, 1971: 171-175.
[7] Neira J, Tardos J D. Data association in stochastic mapping using the joint compatibility test[J]. IEEE Transactions on Robotics and Automation, 2001, 17(6): 890-897.
[8] Bailey T, Nebot E M, Rosenblatt J K, et al. Data association for mobile robot navigation: A graph theoretic approach[C]//IEEE International Conference on Robotics and Automation. Piscataway, NJ, USA: IEEE, 2000: 2512-2517.
[9] Bailey T. Mobile robot localization and mapping in extensive outdoor environment[D]. Sydney, Australia: The University of Sydney, 2002.
[10] 王万良.人工智能及其应用[M].北京:高等教育出版社, 2008: 281-313. Wang W L. Artificial intelligence principles and applications[ M]. Beijing: Higher Education Press, 2008: 281-313.
[11] 王小平,曹立明.遗传算法理论、应用与软件实现[M].西 安:西安交通大学出版社,2005: 88-108. Wang X P, Cao L M. Genetic algorithm theory, application and software realization[M]. Xi’an: Xi’an Jiaotong University Press, 2005: 88-108.
