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.