基于轮式里程计辅助视觉的无人车重定位算法OA

To address the issue of SLAM-based visual relocalization failure in unmanned vehicles under visual degradation conditions,an adaptive collaborative relocalization algorithm was proposed,which combined wheel odometry(WO)with visual odometry(VO).This algorithm constructed a WO/VO fusion model based on error-state Kalman filter(ES-KF),enhancing the positioning accuracy of the unmanned vehicle's path planning.To tackle the challenge of insufficient visual feature points that hinder relocalization during unmaned vehicle operation,an adaptive collaborative relocalization algorithm for WO/VO was proposed.The system included a switching mechanism between WO/VO and WO,improving the continuity of the unmanned vehicle's localization and the reliability of path planning.Experiments were conducted and compared on the developed unmanned vehicle platform under various conditions.The results demonstrate that the ES-KF-based WO/VO fusion method significantly improves localization performance.The absolute trajectory error(ATE)is reduced by 39.1%and 59.2%,respectively,compared to VO-only and WO-only methods,and path overlap is increased by 9.2%and 11.5%,respectively.The algorithm also successfully achieves WO-assisted adaptive collaborative relocalization in visual degradation scenarios.Compared to the extended Kalman filter(EKF)and the unscented Kalman filter(UKF),the ES-KF demonstrates superior performance in both trajectory accuracy and pose estimation stability,significantly enhancing path fusion effectiveness and system robustness.