摘要
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 proposed, 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 environment with external disturbance, and the performance of the proposed algorithm is superior to other algorithms.
出处
《计算机与现代化》
2016年第2期104-106,111,共4页
Computer and Modernization
基金
国家自然科学基金资助项目(61203365)
江苏省自然科学基金资助项目(BK2012149)
中央高校基本科研业务费专项基金资助项目(2011B04614)
