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An improved H-infinity unscented FastSLAM with adaptive genetic resampling
Robotics and Autonomous Systems ( IF 4.3 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.robot.2020.103661
Ming Tang , Zhe Chen , Fuliang Yin

Abstract The FastSLAM is a typical tracking algorithm for SLAM, but it often suffers from the low tracking accuracy. To mitigate the problem, an improved H-Infinity unscented FastSLAM (IHUFastSLAM) with adaptive genetic resampling is proposed in this paper. Specifically, the H-Infinity unscented Kalman filter algorithm is improved using an adaptive factor and is employed as importance sampling in particle filter. Next, the process noise and the measurement noise are estimated by a time varying noise estimator. Moreover, an adaptive genetic algorithm is used to complete the resampling of particle filter. Finally, the improved H-Infinity UFastSLAM with adaptive genetic resampling is proposed to complete robot tracking. The proposed algorithm can track robot with good accuracy, and obtain reliable state estimation in SLAM. Simulation results reveal the validity of the proposed algorithm.

中文翻译:

具有自适应遗传重采样的改进的 H 无穷大无味 FastSLAM

摘要 FastSLAM是一种典型的SLAM跟踪算法,但其跟踪精度往往较低。为了缓解这个问题,本文提出了一种具有自适应遗传重采样的改进的 H-Infinity 无味 FastSLAM(IHUFastSLAM)。具体而言,H-Infinity 无迹卡尔曼滤波器算法使用自适应因子进行了改进,并被用作粒子滤波器中的重要性采样。接下来,过程噪声和测量噪声由时变噪声估计器估计。此外,采用自适应遗传算法完成粒子滤波器的重采样。最后,提出了改进的具有自适应遗传重采样的 H-Infinity UFastSLAM 来完成机器人跟踪。提出的算法可以很好地跟踪机器人,并在SLAM中获得可靠的状态估计。
更新日期:2020-12-01
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