Fruit Mapping with Shape Completion for Autonomous Crop Monitoring

Abstract

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.

Plant phenotyping relevance

果実の画像・RGB-Dデータから形状と体積を推定する手法が研究の中心であり、植物表現型の取得・抽出に直接該当する。

Code and data availability

The paper describes fruit mapping and superellipsoid shape estimation using simulated Gazebo scenarios and glasshouse RGB-D recordings, but contains no public dataset, image, code, or model release statement. The only URLs present (the Springer DOI chapter and Ceres solver) are cited prior work and a generic thirdparty

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