变分贝叶斯自适应容积卡尔曼的SLAM算法

Adaptive cubature Kalman filtering SLAM algorithm based on variational Bayes

摘要:

在观测噪声参数未知或变化时,传统的同步定位与建图（SLAM）算法性能会下降,为了让SLAM算法性能在上述条件下不受影响同时具有较高的精度,基于此提出了一种基于变分贝叶斯噪声自适应容积卡尔曼滤波的SLAM算法（VB-ACKF-SLAM）.该算法采用逆Wishart分布对未知观测噪声参数建模,采用容积积分方法近似非线性变换的均值和方差,并利用变分贝叶斯滤波实现对移动机器人状态和未知观测噪声参数的联合后验概率的估计.该算法有效地解决了在观测噪声参数未知或变化时,传统滤波算法出现的滤波发散问题.仿真实验结果表明,在观测噪声参数未知或变化时,与基于容积卡尔曼滤波的SLAM算法（CFK-SLAM）、无迹卡尔曼滤波的SLAM算法（UKF-SLAM）、扩展卡尔曼滤波的SLAM算法（EKF-SLAM）相比,VB-ACKF-SLAM算法的定位准确率得到了较大的提高,证明了该算法的有效性.

Abstract:

When the observed noise parameters are unknown or change with time, the performance of the traditional SLAM algorithm will decline. In this paper, an SLAM algorithm is presented based on variational Bayes noise adaptive cubature Kalman filter (VB-ACKF). The inverse Wishart distribution was used to model the observed noise parameters, and the nonlinear variational Bayes filter was utilized to estimate the joint posteriori probability of the mobile robot state and the unknown observation noise parameter. The proposed algorithm effectively solved the problem of filtering divergence of traditional filtering algorithms when the observed noise parameter was unknown or changing. Simulation results show that the positioning accuracy of VB-ACKF-SLAM algorithm was greatly improved compared with the SLAM method based on the cubature Kalman filter (CKF-SLAM), the unscented Kalman filter SLAM algorithm (UKF-SLAM), and the extended Kalman filter SLAM algorithm (EKF-SLAM) when the observed noise parameters are unknown or changing. The effectiveness of the algorithm is proved.