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The paper introduces a multi-modal dataset of synchronized stereo vision, LiDAR, and GNSS data collected from a tractor during real baling operations to facilitate open research in autonomous forage harvesting. They implement and validate a real-time centroid-based windrow-following method on an NVIDIA Jetson AGX Orin using the dataset. Results show strong agreement between stereo and LiDAR depth measurements, suggesting that low-cost stereo sensors can effectively replace LiDAR in this application.
Low-cost stereo vision can rival LiDAR for real-time windrow detection, paving the way for more accessible autonomous farming solutions.
Proprietary design in commercial windrow-detection systems restricts transparency and limits progress in open autonomous forage-harvesting research. We present a multi-modal dataset combining stereo vision and LiDAR from tractor-mounted sensors during real baling operations. The dataset includes synchronized sensor data with GNSS trajectories, partly released as ROS2 Humble bags on Zenodo, with additional data available on request. Using this dataset, we implement a real-time (>20 Hz) centroid-based windrow-following method on an NVIDIA Jetson AGX Orin. Across the critical 4-10 m guidance range, stereo and LiDAR depth measurements show strong agreement (0.965 +/- 0.021), indicating that low-cost stereo sensors can approach LiDAR performance. Our open-source ROS 2 pipeline provides a reproducible benchmark for GPS-free windrow detection and supports development of practical autonomous forage-harvesting systems. Dataset: https://zenodo.org/records/17486318
