果樹園の長期モニタリングのための時空間メトリック・セマンティックマッピング：手法とデータセット
Spatio-Temporal Metric-Semantic Mapping for Persistent Orchard Monitoring: Method and Dataset
LiDARとRGBを融合して果実の3D位置を推定し、位置・見た目・トポロジー情報を活用した4D対応付けで成長を追跡するシステムを提案。実データで96.9%の果実計数精度を達成し、多種果実の成長期データセットを公開した。
著者: Jiuzhou Lei, Ankit Prabhu, Xu Liu, Fernando Cladera, Mehrad Mortazavi, Reza Ehsani, Pratik Chaudhari, Vijay Kumar
分類: cs.RO
原文アブストラクト
Monitoring orchards at the individual tree or fruit level throughout the growth season is crucial for plant phenotyping and horticultural resource optimization, such as chemical use and yield estimation. We present a 4D spatio-temporal metric-semantic mapping system that integrates multi-session measurements to track fruit growth over time. Our approach combines a LiDAR-RGB fusion module for 3D fruit localization with a 4D fruit association method leveraging positional, visual, and topology information for improved data association precision. Evaluated on real orchard data, our method achieves a 96.9% fruit counting accuracy for 1,790 apples across 60 trees, a mean fruit size estimation error of 1.1 cm, and a 23.7% improvement in 4D data association precision over baselines. We publicly release a multimodal dataset covering five fruit species across their growth seasons at https://4d-metric-semantic-mapping.org/
