多新息抗差-自适应卡尔曼滤波定位算法研究

摘要

In the positioning process,the positioning accuracy of mobile robot drops sharply because of the sensor measurement error and the pose error caused by the robot model.In view of this phenomenon,a new algorithm based on multi-innovation Kalman filtering(MR-AKF) is proposed.Based on the standard Kalman filter,when the measured value of the sensor is error,the robust weighting factor is introduced,and the estimation accuracy of the filter is improved by changing the weight of the error measurement value;when the robot pose is error,an adaptive factor is introduced to resist the filtering divergence caused by the pose error by adjusting the size of the state covariance matrix.At the same time,introduce multi-innovation which is the innovation vector of multiple moments to further improve the accuracy of the nonlinear system.The experimental results show that the filtering algorithm can effectively improve the positioning accuracy in the presence of measurement errors and pose errors.%针对移动机器人在定位过程中,由传感器测量误差和机器人模型引起的位姿误差导致系统定位精度急剧下降的问题,提出了一种多新息卡尔曼滤波算法.在标准卡尔曼滤波的基础上,当传感器测量值存在误差时,引入抗差权因子,通过改变误差测量值的权值提高滤波器的估计精度;当机器人位姿存在误差时,引入自适应因子,通过调整状态协方差矩阵的大小抵制位姿误差引起的滤波发散.同时,引入了多新息,即多个时刻的新息向量,进一步提高此非线性系统的精度.实验表明:当存在测量误差和位姿误差时,该滤波算法能有效提高定位精度.