论文标题
果实映射，形状完成，以进行自动作物监测
Fruit Mapping with Shape Completion for Autonomous Crop Monitoring
论文作者
论文摘要
由于植物的复杂结构，自主作物监测是一项艰巨的任务。叶子的遮挡可能使得无法对所有水果（例如胡椒植物）获得完整的看法。因此，从部分信息中准确估算水果的形状和体积对于实现进一步的高级自动化任务，例如产量估计和自动化水果取货至关重要。在本文中，我们提出了一种在植物上绘制果实并通过与超ellipsoid相匹配的形状的方法。我们的系统段中的图像中的果实并使用口罩来生成果实的点云。为了结合获得点云的序列，我们利用了一个实时的3D映射框架，并基于截断的签名距离字段建立了水果地图。我们从该地图中聚集了果实，并使用优化的超透明素体进行匹配以获得准确的形状估计。在我们的实验中，我们在各种模拟场景中使用配备了RGB-D相机的机器人臂显示，我们的方法可以准确估计水果量。此外，我们还提供了在商业玻璃房环境中记录的数据的估计水果形状的定性结果。
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.
