机器人在未知环境工作时经常会受到外部干扰的影响,易导致常规SLAM算法定位失败,因此提高其鲁棒性是研究的关键. 针对这一问题,提出一种改进的鲁棒SLAM算法,在应对外部干扰时,同时对系统状态的先验估计误差协方差和观测噪声协方差进行调整,从而得到更准确的定位结果. 仿真实验结果表明,所提算法优于现有算法,在存在外部干扰的情况下能更有效地减小机器人定位误差.%When the robot works in an unknown environment , it is often affected by the external disturbance , which will make the localization failure based on the general SLAM algorithm .To deal with this problem , an improved robust SLAM algorithm is pro-posed, to obtain a more accurate positioning result and reduce the effect of the external disturbance , by adjusting the state prior estimate error co-variance and measurement noise co-variance of the system simultaneously .Finally, some simulation experiments are conducted , the results show that the proposed algorithm can decrease the robot localization error more effectively under the en -vironment with external disturbance , and the performance of the proposed algorithm is superior to other algorithms .
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