原文传递
A Novel Data Association Approach for Slam in Dense Features Environment
| 题名： | A Novel Data Association Approach for Slam in Dense Features Environment |
| 正文语种： | 中文 |
| 作者： | Yao Cong Du Jianyu Zhao Defang Wang Ran |
| 作者单位： | FAW Group Corporation R&D Center |
| 关键词： | SLAM data association K-means clustering JCBB ICNN |
| 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 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. |
| 会议日期： | 20171024 |
| 会议举办地点： | 上海 |
| 会议名称： | 第19届亚太汽车工程年会暨2017中国汽车工程学会年会 |
| 出版日期： | 1024-01-20 |
| 母体文献： | 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集 |
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