基于迭代平方根UKF的SLAM算法

SLAM Algorithm Based on Iterated SquareUnscented Kalman Filter

针对载体在大范围、多路标特征环境下,应用于SLAM的无迹卡尔曼滤波(unscented Kalman filter,UKF)所产生的Sigma点会逐渐偏离真实状态估计值的问题,提出了迭代测量更新的UKF-SLAM算法。当获得某一时刻观测值后,使用经过更新的状态估计值和协方差重新产生Sigma采样点,并进行UT变换,计算滤波参数。仿真结果表明,与平方根UKF-SLAM算法相比,能将载体状态估计误差在x轴和y轴分别降低约19%和21%,使载体状态估计值更接近真实值,并加快SLAM收敛速度。

The sigma points whose center and covarianee were predictive value and predictive variance would deviate from true value while UKF applied for large-scale outdoor SI.AM. This paper advantage of an iteration mechanism in update equations of unscented SLAM. Updated state estimate and covariancere applied to new sigma points while the observation obtained for one instant. Filtering parameters calculated after nscented of these sigma points, the state estimate value close to the true one, and efficiency of data association and accuracy of map building. The simulation results show that this proposed method could reduce error approximately 19% in x while 21% in y while accelerate convergence speed of SLAM.