构建农田生长与光照变化的多模态数据集，助力农业机器人精准导航
AgriChrono: A Multi-modal Dataset Capturing Crop Growth and Lighting Variability with a Field Robot
- 用多传感器机器人平台同步采集RGB、深度、激光雷达等数据
- 收集18TB数据，覆盖油菜全生长周期和多样光照条件
- 公开数据集，推动农业场景3D重建与机器人自主导航研究
人工智能与机器人技术的进步加速了农业领域的创新，尤其在机器人导航与三维数字孪生创建方面。然而，真实农田中非刚性运动（如风致晃动）、剧烈光照变化及作物生长导致的形态演变等复杂因素，使得缺乏真实野外环境下的高质量数据集，严重制约了鲁棒性AI模型的发展。本文提出AgriChrono——一个模块化机器人数据采集平台及多模态数据集，集成多种传感器，实现远程、时间同步的RGB、深度、LiDAR、IMU与位姿数据采集，支持真实农田环境下的长期重复采集。我们成功采集了18TB数据，完整记录了油菜作物在不同光照条件下的整个生长周期。在该数据集上对当前主流3D重建方法进行基准测试，揭示了在动态非刚性农田场景中实现高保真重建的严峻挑战。该基准验证了AgriChrono作为提升模型泛化能力的关键资源，其公开发布有望显著加速精准农业领域的研究与开发。代码与数据已开源：https://github.com/StructuresComp/agri-chrono
原文摘要 · Abstract (English)
Advances in AI and Robotics have accelerated significant initiatives in agriculture, particularly in the areas of robot navigation and 3D digital twin creation. A significant bottleneck impeding this progress is the critical lack of "in-the-wild" datasets that capture the full complexities of real farmland, including non-rigid motion from wind, drastic illumination variance, and morphological changes resulting from growth. This data gap fundamentally limits research on robust AI models for autonomous field navigation and scene-level dynamic 3D reconstruction. In this paper, we present AgriChrono, a modular robotic data collection platform and multi-modal dataset designed to capture these dynamic farmland conditions. Our platform integrates multiple sensors, enabling remote, time-synchronized acquisition of RGB, Depth, LiDAR, IMU, and Pose data for efficient and repeatable long-term data collection in real-world agricultural environments. We successfully collected 18TB of data over one month, documenting the entire growth cycle of Canola under diverse illumination conditions. We benchmark state-of-the-art 3D reconstruction methods on AgriChrono, revealing the profound challenge of reconstructing high-fidelity, dynamic non-rigid scenes in such farmland settings. This benchmark validates AgriChrono as a critical asset for advancing model generalization, and its public release is expected to significantly accelerate research and development in precision agriculture. The code and dataset are publicly available at: https://github.com/StructuresComp/agri-chrono
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