Fruit Mapping with Shape Completion for Autonomous Crop Monitoring

TL;DR

This paper introduces a novel method for mapping and shape estimation of fruits on plants using partial image data, superellipsoid matching, and 3D mapping, to improve autonomous crop monitoring tasks.

Contribution

It presents a new approach combining 3D mapping and superellipsoid fitting for accurate fruit shape and volume estimation from partial observations.

Findings

Accurately estimates fruit volumes in simulated scenarios.

Provides qualitative shape estimates from real greenhouse data.

Integrates real-time 3D mapping with shape completion techniques.

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.