【摘要】 提出了一种基于直线检测模型的多传感器数据融合航向角随机误差校正方法，旨在提高农林环境下低成本传感器组成的定位平台的精度。该方法通过调整直线检测阈值来实现状态的动态调整，以提高导航系统的鲁棒性和精确性。再将多传感器数据通过卡尔曼滤波融合，实现航向角随机误差的校正。试验结果表明，该方法在不同路径和速度下能有效降低航向角误差提高定位精度。在直线行进试验中，本方法的定位精度保持在5 cm以内，航向角误差在5°以内。在矩形行进试验中，本方法的轨迹与差分RTK方法相近，平均误差仅为2.7 cm,标准差为3.9 cm。这一航向角校正方法为农业机械和车辆环境中的自主操作提供了有力支持。它能够适应不同的环境条件，提高导航系统的性能和测量准确性。更多还原

【Abstract】 This paper presents a yaw angle random error correction method based on a line detection model and multisensor data fusion, aimed at enhancing the accuracy of a low-cost sensor-equipped positioning platform in agricultural and forestry environments. The method achieves dynamic state adjustment by tuning the line detection threshold to improve the robustness and precision of the navigation system. Subsequently, it fuses multisensor data using Kalman filtering to correct yaw angle random errors. Experimental results demonstrate the method′s effectiveness under various paths and velocities. In straight-line progress experiments, the positioning accuracy of this method remains within 5 cm, with a yaw angle error within 5°. In rectangular progress experiments, the trajectories closely resemble those of the differential RTK method, with an average error of only 2.7 cm and a standard deviation of 3.9 cm. This yaw angle correction method provides robust support for autonomous operations in agricultural machinery and vehicle environments. It is adaptable to different environmental conditions, thereby enhancing the performance and measurement accuracy of navigation systems.更多还原