RTK and IMU Fusion Positioning Technology for Orchard Robots

Positioning is fundamental for enabling intelligent operations of orchard equipment. However, satellite-based positioning signals are prone to occlusion and multipath effects caused by tree canopies and surrounding obstacles, resulting in degraded localization accuracy. To address these challenges in orchard environments, this paper introduces a measurement-deviation-based extended Kalman filter fusion algorithm. The proposed method enhances state estimation accuracy by adaptively adjusting measurement noise covariances and mitigating bias accumulation due to sensor drift and intermittent signal outages. Consequently, it offers improved robustness and adaptability in real-time state estimation. Experimental results demonstrate that this fusion scheme significantly enhances both the accuracy and real-time responsiveness of Inertial Measurement Unit (IMU) and Real-Time Kinematic (RTK) data fusion, thereby satisfying the stringent positioning requirements of orchard applications.