AgriChrono: フィールドロボットによる作物成長と照明変動を捉えるマルチモーダルデータセット
AgriChrono: A Multi-modal Dataset Capturing Crop Growth and Lighting Variability with a Field Robot
風による非剛体運動や照明変化、成長に伴う形状変化を含む実農場の動的環境を長期収録するロボットプラットフォームと、18TBのマルチモーダルデータセットを構築し、3D再構成手法のベンチマークを行った。
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著者: Jaehwan Jeong, Tuan-Anh Vu, Mohammad Jony, Shahab Ahmad, Md. Mukhlesur Rahman, Sangpil Kim, M. Khalid Jawed
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
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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