Journal of Changshu Institute of Technology2024，Vol.38Issue(2)：63-70,8.
基于动态步长梯度下降的EKF-SLAM改进算法
Improved EKF-SLAM Algorithm Based on Dynamic Step-size Gradient Descent
摘要
Abstract
The increasingly complex environment for modern heavy-duty goods transportation requires AGV-SLAM algorithms to have higher robustness and accuracy in order to achieve precise loading and unloading operations. Therefore, this paper proposes an improved EKF-SLAM algorithm based on dynamic step-size gradient descent. Firstly, the AGV motion process is modeled and analyzed, and the kinematic and observation models of the AGV are established. Considering the truncation error problem that may be introduced by the EKF-SLAM algorithm during state estimation, this paper proposes an improved algorithm to address the issue of low accuracy in the prediction estimation of AGV's own pose and environmental feature positions. The improved algorithm introduces gradient descent algorithm and dynamic step-size strategy, where the step-size is positively correlated with the AGV's forward speed and sampling time. Simulation results show that compared with the standard EKF-SLAM algorithm, the improved algorithm can quickly generate better-estimated landmarks and paths in the parking lot data set, thus improving the robustness and accuracy of traditional algorithms to a certain extent, and carries reference value for applications in the industry .关键词
同时定位与地图创建/动态步长/扩展卡尔曼滤波/停车场数据集/AGVKey words
simultaneous localization and mapping/dynamic step size/extended kalman filter/parking lot dataset/AGV分类
天文与地球科学引用本文复制引用
索会恒,魏博思,饶睿,胡强,钟璟,杨腾胜,吴剑..基于动态步长梯度下降的EKF-SLAM改进算法[J].Journal of Changshu Institute of Technology,2024,38(2):63-70,8.基金项目
南昌航空大学研究生创新专项"ROS环境下的移动机器人实时定位导航系统设计"(YC2021-S679) （YC2021-S679）
南昌航空大学研究生创新专项"基于多智能体的多机器人协同任务规划技术研究"(YC2021-037) （YC2021-037）
