BAJML algorithm based on joint likelihood in data association

Chuangfeng Huai; Yan Zhao; Zhiyu Jin

doi:10.1117/12.2618214

29 November 2021 BAJML algorithm based on joint likelihood in data association

Chuangfeng Huai, Yan Zhao, Zhiyu Jin

Proceedings Volume 12080, 4th International Symposium on Power Electronics and Control Engineering (ISPECE 2021); 1208011 (2021) https://doi.org/10.1117/12.2618214
Event: 4th International Symposium on Power Electronics and Control Engineering (ISPECE 2021), 2021, Nanchang, China

Abstract

In the process of SLAM (Simultaneous Localization And Mapping), an essential link is data association. Data association provides the basis for state estimation to ensure that the robot can make full use of the obtained environmental information to realize positioning and mapping. Aiming at the problems of low association accuracy of current single target data association algorithm and difficult calculation of multi-target data association algorithm, a bat optimized multi-target data association algorithm based on joint maximum likelihood is proposed to ensure accurate data association processing results, and reduce the computational complexity of SLAM algorithm. It is based on joint maximum likelihood, The multi-objective data association problem is transformed into an optimization problem to solve the optimal combination, and the bat optimization algorithm is used to search the optimal data association. The slam process of the mobile robot experiments show that the proposed bat optimized data association algorithm improves the rapidity and accuracy of slam process of mobile robot.

Citation Download Citation

Chuangfeng Huai, Yan Zhao, and Zhiyu Jin "BAJML algorithm based on joint likelihood in data association", Proc. SPIE 12080, 4th International Symposium on Power Electronics and Control Engineering (ISPECE 2021), 1208011 (29 November 2021); https://doi.org/10.1117/12.2618214

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