【摘 要】
：
The correct data association increases robustness and enhances accuracy in SLAM.It attracts much attention over the past decades.In this paper,an efficient data association approach for SLAM based on
【机 构】
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China FAW Group Corporation R&D Center
【出 处】
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第19届亚太汽车工程年会暨2017中国汽车工程学会年会
论文部分内容阅读
The correct data association increases robustness and enhances accuracy in SLAM.It attracts much attention over the past decades.In this paper,an efficient data association approach for SLAM based on JCBB and K-means clustering is presented.Firstly,in order to decrease the computation complexity and preserve high matching accuracy in dense feature environment,the small correlative measurements are separated into several groups by K-means clustering.The number of groups is depended on the characteristics of the environment.Secondly,each group local correlations are produced by JCBB and ICNN,respectively.Finally,all local correlations are put together to find the most joint compatibility one as the global correlation.The experimental results demonstrate that the proposed approach can achieve high matching accuracy and low computation time than the existing state-of-the-art methods.
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