基于区间分析无迹粒子滤波的移动机器人SLAM方法

SLAM Method for Mobile Robot Based on Interval Analysis Unscented Particle Filter

移动机器人同时定位与地图创建算法SLAM中,非线性系统线性化处理和雅可比矩阵计算导致运算量大、系统状态估计精度较低。为此提出了一种基于区间分析的无迹粒子滤波同时定位与地图创建的方法。利用基于区间分析的区间粒子滤波进行位姿估计,降低了定位需要的粒子数;并采用无迹卡尔曼滤波更新地图中的特征,提高了地图创建的精度。在同等粒子数的情况下,改进了SLAM的精度,减少了运算时间,实验结果验证了方法的有效性。

In particle filter simultaneous localization and mapping method（SLAM） for mobile robots,linearization of nonlinear systems and derivation of the Jacobian matrices lead to large computing and system state estimation problem of low accuracy.To solve this problem,a SLAM method based on interval analysis and unscented particle filter was proposed.The number of particles was seriously reduced based on the interval analysis for pose estimation.The nature landmarks were updated through unscented Kalman filter to improve map estimation accuracy.This approach improved the state estimation accuracy and computational effort with a smaller number of particles than previous approaches.The results indicated that the proposed method was valid.