基于Cholesky分解的改进自适应EKF-SLAM算法

A Novel Adaptive EKF-SLAM Algorithm Via Cholesky Decomposition

针对EKF-SLAM(扩展卡尔曼滤波即时定位与地图构建)算法运行精度较低且速度较快,提出基于Cholesky分解的改进Sage-Husa自适应EKF-SLAM算法,使得移动智能体在进行路径规划和地图构建的同时,能够在短时间内找到最佳或者次优路径。该方法选用改进Sage-Husa自适应滤波算法,引入遗忘因子来提高算法的计算速度,并且通过Cholesky分解来提高算法稳定性和精确性。仿真表明,基于Cholesky分解的改进Sage-Husa自适应EKF-SLAM算法比传统EKF-SLAM和改进Sage-Husa自适应EKF-SLAM算法更加精确快速,鲁棒性更好。

The EKF-SLAM(Extended Kalman Filter Simultaneous localization and mapping) algorithm has low precision and long speed.Based on the improved Sage-Husa adaptive EKF-SLAM algorithm,the mobile agent is used for path planning and map construction.This algorithm enables the mobile agent to find the best or sub-optimal path in a short time while performing path planning and map construction.The method uses the improved Sage-Husa adaptive filtering algorithm and introduces the forgetting factor to improve the calculation speed of the algorithm.The simulation shows that the improved Sage-Husa adaptive EKF-SLAM algorithm is more accurate,faster and more robust than the traditional EKF-SLAM.