基于改进多新息理论的EKF-SLAM算法  

EKF-SLAM algorithm based on improved multi-innovation theory

作　　者：周振 王冬青 许柏杨 许崇立 ZHOU Zhen;WANG Dongqing;XU Boyang;XU Chongli(Qingdao University,automation,Qingdao Shandong 266071,China;Qingdao University,electrical engineering,Qingdao Shandong 266071,China)

机构地区：[1]青岛大学自动化工程学院,山东青岛266071 [2]青岛大学电气工程学院,山东青岛266071

出　　处：《自动化与仪器仪表》2020年第6期21-25,共5页Automation & Instrumentation

基　　金：国家自然科学基金项目(No.61873138,61573205);国家自然科学基金资助项目:复杂网络拓扑与参数的辨识(No.61573295);国家自然科学基金资助项目:基于数据特征的多模态过程辨识建模方法(No.61873138)。

摘　　要：扩展卡尔曼滤波(EKF)是移动机器人实时定位和地图构建(SLAM)的经典方法。但是当系统为强非线性时,EKF将违背局部线性化假设,导致估计误差增加,并最终降低移动机器人定位和建图的准确性。为了解决上述问题,提出了一种改进MI-EKF-SLAM算法,在每个滤波过程中,将当前时刻的新息向量扩展为过去多个时刻的新息向量,并且将扩展新息向量中每个时刻的观测值都替换为上一时刻的观测值。仿真结果表明,改进算法减少了状态估计误差,实现了更高的定位和建图精度,并且得到改进MI-EKF-SLAM算法中最佳新息长度为3。Extended Kalman filtering(EKF)is a classic method in simultaneous localization and mapping(SLAM)for mobile robots.However,when the system is strongly nonlinear,EKF will violate the local linear assumption,cause the error to increase,and ultimately reduce the accuracy of positioning and mapping of mobile robots.To solve the above problems,a multi-innovation with Forgetting factor based EKF-SLAM(MI-EKF-SLAM)is investigated.At each filtering step,the MI-EKF-SLAM expand the scalar innovation at current step to an innovation vector containing innovation of current and previous steps,and And the observations at each moment in the extended innovation vector are replaced with the observations from the previous moment.The simulation results show that the explored MIEKF-SLAM reduces the state estimation errors,and achieves higher accuracy in positioning and mapping.And the best innovation length in the improved MI-EKF-SLAM algorithm is 3.

关 键 词：多新息理论 扩展卡尔曼滤波 SLAM 新息长度 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]