基于Cholesky分解的改进自适应EKF-SLAM算法  

A Novel Adaptive EKF-SLAM Algorithm Via Cholesky Decomposition

作　　者：程璐 黄宜庆 CHENG Lu;HUANG Yiqing(School of Electrical Engineering,Anhui Polytechnic University,Wuhu 241000,China)

机构地区：[1]安徽工程大学电气工程学院,安徽芜湖241000

出　　处：《安徽工程大学学报》2020年第2期59-64,共6页Journal of Anhui Polytechnic University

基　　金：国家自然科学基金资助项目(61572032);安徽省高校自然科学研究重点基金资助项目(KJ2018A0110);安徽工程大学中青年拔尖人才基金资助项目(2016BJRC004)。

摘　　要：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.

关 键 词：路径规划 EKF-SLAM Sage-Husa自适应算法 CHOLESKY分解 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]