AgriGen: 農業ロボティクス向け大規模フォトリアルシーン生成フレームワーク
AgriGen: Large-Scale Scene Generation Framework for Photorealistic Agricultural Robotics Simulation
Isaac Sim上に構築したROS統合フレームワークで、作物列・果樹園・ぶどう園などの農業環境をフォトリアルかつ大規模に手続き生成し、物理シミュレーションとドメインランダム化を可能にする。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Utkarsh Bajpai, Serge Tleiji, Cédric Pradalier, Stéphanie Aravecchia
分類: cs.RO, cs.GR
原文アブストラクト
Agricultural robotics is advancing rapidly, yet progress remains constrained by limited field access, lack of control over field conditions, geographic variability, and seasonal crop cycles. These factors make it difficult and costly to acquire diverse agricultural datasets, resulting in limited evaluation and reduced system robustness. While other robotics domains have scaled learning and evaluation through high-fidelity simulation, agricultural robotics still lacks comparably capable tools. In this paper, we present a ROS-integrated framework, built on Isaac Sim, for large-scale procedural generation of agricultural environments. The framework supports photorealistic rendering, physics simulation, and domain randomization at scales relevant to robotics research, with built-in support for row crops, orchards, and vineyards and straightforward extensibility to additional crop categories. Project Page: https://baj31415.github.io/agrigen/