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Towards plant monitoring through Next Best View

机译:通过Next Best View进行工厂监控

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Monitoring plants using leaf feature detection is a challenging perception task because different leaves, even from the same plant, may have very different shapes, sizes and deformations. In addition, leaves may be occluded by other leaves making it hard to determine some of their characteristics. In this paper we use a Time-of-Flight (ToF) camera mounted on a robot arm to acquire the depth information needed for plant leaf detection. Under a Next Best View (NBV) paradigm, we propose a criterion to compute a new camera position that offers a better view of a target leaf. The proposed criterion exploits some typical errors of the ToF camera, which are common to other 3D sensing devices as well. This approach is also useful when more than one leaf is segmented as the same region, since moving the camera following the same NBV criterion helps to disambiguate this situation.
机译:使用叶子特征检测来监视植物是一项具有挑战性的感知任务,因为即使来自同一植物的不同叶子也可能具有非常不同的形状,大小和变形。此外,树叶可能会被其他树叶遮挡,从而难以确定其某些特征。在本文中,我们使用安装在机器人手臂上的飞行时间(ToF)相机来获取植物叶片检测所需的深度信息。在下一个最佳视图(NBV)范式下,我们提出了一个准则来计算新的相机位置,以提供对目标叶子的更好视图。提出的标准利用了ToF相机的一些典型错误,这些错误对于其他3D感应设备也很常见。当将多个叶子分割为同一区域时,此方法也很有用,因为按照相同的NBV标准移动相机有助于消除这种情况的歧义。

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