计算机科学 ›› 2017, Vol. 44 ›› Issue (8): 306-311.doi: 10.11896/j.issn.1002-137X.2017.08.053
罗元,苏琴,张毅,管国伦
LUO Yuan, SU Qin, ZHANG Yi and GUAN Guo-lun
摘要： 为缓解移动机器人同步定位与构图(Simultaneous Localization and Mapping,SLAM)在恶劣噪声干扰下存在估计精度低、不一致及鲁棒性差的问题,提出一种新颖的基于迭代无迹H∞滤波的SLAM算法。所提算法将无迹变换融入到扩展H∞滤波中,以此估计系统状态均值和协方差,无需推导Jacobian矩阵,避免了线性化误差积累,增强了算法的数值稳定性；此外,通过迭代更新方式,利用观测信息不断校正系统状态均值和协方差,进一步减小估计误差。在仿真实验中,在不同环境和不同噪声下对比分析所提算法、EKF-SLAM、UKF-SLAM及CEHF-SLAM。结果表明所提算法在不同恶劣噪声干扰下依然能保持高的估计精度和强鲁棒性,并能适应不同的环境,是一种有效且可行的SLAM算法。
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