Navigation System for Intelligent Harvester Based on Tightly Coupled Adaptive Fusion and Cooperative Control
Autonomous navigation of harvesters in hilly and mountainous terrain faces two major challenges: sensor discrepancies among multiple sources and depth distortion caused by terrain slopes. This paper proposes a tightly coupled vision–inertial–depth navigation and control system to address these issues. The system fuses visual features with inertial data within an adaptive extended Kalman filter framework that dynamically adjusts sensor weights to resolve conflicts from illumination changes and inertial drift. It also incorporates a real-time depth compensation model based on vehicle attitude to correct spatial mapping distortions during slope operations. Additionally, a multi-controller coordination strategy integrates steering, speed, and header height to align state estimation with control execution. Field experiments show that the system achieves a lateral positioning error of 3.4 cm—48.5% and 81.5% lower than pure-vision and pure-inertial approaches, respectively-and remains within 9.5 cm even in degraded scenarios. These results demonstrate the system’s ability to deliver high-precision navigation and stable operation on complex terrain.