广东石油化工学院学报2024，Vol.34Issue(3)：79-85,7.
基于高阶扩维卡尔曼滤波器的SLAM算法研究
SLAM Algorithm Based on Higher-order Extended Kalman Filter
摘要
Abstract
A method based on Higher-Order Extended Kalman Filter(HEKF)is proposed to address the limitations of Extended Kalman Filter(EKF)in dealing with highly nonlinear systems,large errors in simultaneous localization and mapping(SLAM)for robots,weak disturbance rejection,and suboptimal motion estimation for vehicles.The HEKF algorithm introduces hidden variables to mitigate rounding errors,linearizes the motion model of the vehicle,establishes a linear relationship between the system variables and hidden variables,and transforms the system state model into a linear form.The observation model is also equivalently rewritten.Consequently,the motion model of the vehicle is reformulated to comply with the linear form of the Kalman filter.Simulation results demonstrate that the HEKF-SLAM algorithm exhibits superior performance and high accuracy.关键词
SLAM/特征估计/卡尔曼滤波器Key words
SLAM/feature estimation/Kalman Filter分类
计算机与自动化引用本文复制引用
蔡彦斌,刘继新,邓立伟,曹月..基于高阶扩维卡尔曼滤波器的SLAM算法研究[J].广东石油化工学院学报,2024,34(3):79-85,7.基金项目
广东石油化工学院人才引进项目(2020rc32) （2020rc32）
广东省科技创新战略专项资金项目(2023S003042) （2023S003042）
