基于鲁棒非线性卡尔曼滤波的自适应SLAM算法

针对传统非迹卡尔曼滤波算法缺乏在线自适应调整能力，在噪声模型出现误差时滤波精度下降的问题，提出了一种基于鲁棒无迹卡尔曼滤波的同步定位与地图创建算法。该算法引入了一个多维观测噪声尺度因子，能根据观测噪声统计特性的实际变化情况对每种传感器的噪声模型做出自适应调整，使其逼近真实噪声水平，进而将滤波增益调整到一个适当值，实现滤波器的最优估计。SLAM仿真实验结果表明，在噪声统计特性发生变化的情况下，该算法相比其它几种SLAM算法具有更好的自适应能力，估计精度更高，鲁棒性更强。

An Adaptive SLAM Algorithm Based on Robust Unscented Kalman Filter

The traditional unscented Kalman filer is lack of on-line adaptive adjustment ability,and probably decreases the filtering accuracy under the influence of erroneous noisy model.An improved simultaneous localization and mapping(SLAM) algorithm based on robust unscented Kalman filer is proposed.An multidimensional measurement noise scale factor is introduced into the proposed algorithm to adaptively adjust each sensor’s noisy model according to the real changing condition of noisy statistic characteristics,and then the filter gain is rectified to an appropriate value,thus improving the estimation accuracy of the filter.Simulations are performed by using different SLAM algorithms with the time-varying noisy statistics,results show that the proposed algorithm is of better adaptability and estimation accuracy compared with other SLAM algorithms,and its robustness is also improved.