International Journal of Control, Automation and Systems ( IF 3.2 ) Pub Date : 2021-02-18 , DOI: 10.1007/s12555-019-0997-1 Ming Tang , Zhe Chen , Fuliang Yin
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.
中文翻译:
具有遗传重采样的改进的自适应无味FastSLAM
同时定位和映射(SLAM)是智能机器人中的重要课题。本文提出了一种具有遗传重采样的改进的自适应无味FastSLAM。具体而言,改进了自适应无味卡尔曼滤波器(IAUKF)算法作为粒子滤波器的重要性采样,其中自适应因子用于提高系统的跟踪能力,而Huber成本函数可用于降低测量协方差。接下来,通过时变估计器来评估过程噪声和测量噪声。此外,通过改进的遗传算法(GA)在粒子滤波器中进行重采样。最后,提出了改进的自适应无味FastSLAM(IAUFastSLAM)以完成机器人跟踪。该算法具有良好的跟踪性能,并在SLAM中获得了可靠的状态估计。仿真结果表明了该算法的有效性。