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): 217-223..
ZHOU Wu, ZHAO Chun-xia. An Optimized Data Association Solution for SLAM. ROBOT, 2009, 31(3): 217-223..
摘要联合相容分支定界算法（JCBB）存在"计算复杂度高"等缺点．为了优化JCBB算法在准确度和计算复杂度方面的性能，对它进行了三处改进：一是采用互斥准则和最优准则来提高关联的准确度；二是根据机器人的位姿和传感器的测量范围将数据关联限定在局部可能区域中；三是自适应地进行分批数据关联．仿真实验结果表明，优化JCBB算法（OJCBB）在保证准确度的同时大大降低了计算复杂度．Victoria Park Dataset实验表明，OJCBB算法的数据关联结果是可信的，而且OJCBB算法的计算效率远远高于JCBB算法．
Abstract：Joint compatibility branch and bound(JCBB) owns some disadvantages like highly computational complexity. Three improvements are introduced to optimize JCBB’s performance on accuracy and computational complexity.Firstly,data association accuracy is improved with the help of mutual exclusion rule and optimization rule.Secondly,the data association is limited in potential local region,which is determined by robot pose and sensor measurement range.Thirdly,data association is adaptively realized in a divisive manner.Simulation results indicate that optimized JCBB(OJCBB) can ensure accuracy and reduce computational complexity simultaneously.Experimental results with Victoria Park Dataset indicate that the OJCBB data association results are reliable,and the computational efficiency of OJCBB is much better than mat of JCBB.
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