形状補完を用いた果実マッピングによる自律的な作物モニタリング
Fruit Mapping with Shape Completion for Autonomous Crop Monitoring
RGB-Dカメラとロボットアームで果実の点群を取得し、TSDFベースの3Dマップ上で超楕円体フィッティングにより遮蔽された果実の形状・体積を推定する手法を提案した。
著者: Salih Marangoz, Tobias Zaenker, Rohit Menon, Maren Bennewitz
分類: cs.RO
原文アブストラクト
Autonomous crop monitoring is a difficult task due to the complex structure of plants. Occlusions from leaves can make it impossible to obtain complete views about all fruits of, e.g., pepper plants. Therefore, accurately estimating the shape and volume of fruits from partial information is crucial to enable further advanced automation tasks such as yield estimation and automated fruit picking. In this paper, we present an approach for mapping fruits on plants and estimating their shape by matching superellipsoids. Our system segments fruits in images and uses their masks to generate point clouds of the fruits. To combine sequences of acquired point clouds, we utilize a real-time 3D mapping framework and build up a fruit map based on truncated signed distance fields. We cluster fruits from this map and use optimized superellipsoids for matching to obtain accurate shape estimates. In our experiments, we show in various simulated scenarios with a robotic arm equipped with an RGB-D camera that our approach can accurately estimate fruit volumes. Additionally, we provide qualitative results of estimated fruit shapes from data recorded in a commercial glasshouse environment.
