An Optimized Data Association Solution for SLAM

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.