Improved EKF-SLAM Algorithm Based on Dynamic Step-size Gradient Descent
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 .
simultaneous localization and mappingdynamic step sizeextended kalman filterparking lot datasetAGV
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