一种基于蚁群算法的SLAM数据关联方法

Data association method of SLAM based on ant colony algorithm

针对SLAM的数据关联问题,提出了基于蚁群算法的数据关联方法。将SLAM的数据关联问题演化为组合优化问题,通过利用蚁群算法解决组合优化问题的优势,结合JML关联理论,将蚁群算法应用于选择量测和特征的关联集合。详细介绍了该方法的实现步骤,建立了基于蚁群算法的数据关联模型,最后在仿真环境下对其进行了试验。分析结果表明,所提方法在保证关联效率的前提下有效地降低了运算时间,是一种解决SLAM数据关联的可行算法。

A new data association algorithm based on Ant Colony Algorithm （ACA） was proposed to deal with the data association problem for Simultaneous Localization And Mapping （ SLAM）. Using the advantages of ACA in resolving the problem of combination and optimization, the problem of data association was transformed into combinational optimization problem and the ant colony algorithm was used to associate the measurements and features together with Joint Maximum Likelihood （JML） theory. The detailed approach was given and the algorithm model was constructed. At last, the presented algorithm was tested under certain simulation environment. The results show the superiority of the presented method in data association of SLAM. It reduces computation cost and maintains better association efficiency and it is a feasible method to deal with the problem on data association of SLAM.