用低成本摄像头实现拖拉机实时风垄追踪，精度接近激光雷达。
Real-time windrow detection from onboard tractor sensors for automated following
- 融合双目视觉与激光雷达数据，构建开源感知系统
- 在4-10米范围内，双目与激光雷达深度一致率达96.5%
- 支持无GPS环境下的自动割草作业，适合农业自动化研究
商用风垄检测系统多采用专有设计，阻碍了开放自主饲草收割研究的进展。本文提出一种多模态数据集，包含拖拉机作业中同步采集的双目视觉与激光雷达数据，以及GNSS轨迹信息。部分数据以ROS2 Humble格式在Zenodo公开，其余可申请获取。基于该数据集，我们在NVIDIA Jetson AGX Orin上实现了一种实时（>20 Hz）的质心风垄跟随方法。在4-10米关键引导距离内，双目与激光雷达深度测量结果高度一致（相关系数0.965 ± 0.021），表明低成本双目传感器可逼近激光雷达性能。本研究提供的开源ROS2流程为无GPS风垄检测提供了可复现基准，推动实用化自主饲草收割系统发展。
原文摘要 · Abstract (English)
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
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