シミュレーションから実世界へ：ロボットイチゴ収穫のための実地6Dポーズデータセットとベースライン
From Simulation to the Real-World: An In-Field 6D Pose Dataset and Baseline for Robotic Strawberry Harvesting
実際の農場で収集した初のイチゴ6Dポーズ実データセットを提供し、シミュレーションとのギャップを定量化してベースライン性能を示した論文。
著者: Woojung Son, Won Suk Lee, Zijing Huang, Daeun Choi, Catia Silva, Yu She, Yan Gu
分類: cs.CV
原文アブストラクト
Robotic strawberry harvesting requires precise 6D pose estimation; however, collecting 6D pose ground truth in real agricultural fields is inherently challenging. Existing strawberry 6D pose estimation studies have therefore relied mainly on synthetic data, often without sufficient scene-level realism,leaving their performance under real agricultural field conditions unquantified. In this work, we present, to the best of our knowledge, the first real-world 6D pose ground truth dataset of strawberries collected in actual agricultural fields (12,040 images). We also introduce a synthetic dataset rendered in NVIDIA Isaac Sim, featuring scene-level realism and domain randomization. Despite this improved simulation setup, our experiments reveal that a substantial sim-to-real gap persists, underscoring the necessity of real agricultural field data for reliable evaluation. We further quantify the sim-to-real gap through baseline 6D pose estimation results across backbone encoders, serving as a reference for future work.