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An Improved Adaptive Unscented FastSLAM with Genetic Resampling

Ming Tang, Zhe Chen, and Fuliang Yin*
International Journal of Control, Automation, and Systems, vol. 19, no. 4, pp.1677-1690, 2021

Abstract : The simultaneous localization and mapping (SLAM) is a significant topic in intelligent robot. In this paper, an improved adaptive unscented FastSLAM with genetic resampling is proposed. Specifically, the adaptive unscented Kalman filter (IAUKF) algorithm is improved as importance sampling of particle filter, where the adaptive factor is used to improve the tracking ability of system and the Huber cost function is constructed to decrease the measurement covariance. Next, the process noise and the measurement noise are assessed by a time varying estimator. Moreover, the resampling in particle filter is carried out by an improved genetic algorithm (GA). Finally, the improved adaptive unscented FastSLAM (IAUFastSLAM) is proposed to complete robot tracking. The proposed algorithm has good tracking performance and obtains reliable state estimation in SLAM. Simulation results reveal the validity of the proposed algorithm.

Keyword : FastSLAM, genetic algorithm, particle filter, simultaneous localization and mapping (SLAM), time varying noise estimator, unscented Kalman filter.

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