This paper describes a method of estimating the traversability of plant parts covering a path and navigating through them in greenhouses for agricultural mobile robots. Conventional mobile robots rely on scene recognition methods that consider only the presence of objects. Those methods, therefore, cannot recognize paths covered by flexible plants as traversable. In this paper, we present a novel framework of the scene recognition based on image-based semantic segmentation for robot navigation that takes into account the traversable plants covering the paths. In addition, for easily creating training data of the traversability estimation model, we propose a method of generating labels of traversable regions in the images, which we call Traversability masks, based on the robot's traversal experience during the data acquisition phase. It is often difficult for humans to distinguish the traversable plant parts on the images. Our method enables consistent and automatic labeling of those image regions based on the fact of the traversals. We conducted a real world experiment and confirmed that the robot with the proposed recognition method successfully navigated in plant-rich environments.

中文翻译：

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农业移动机器人在植物丰富环境中的语义感知植物可穿越性估计

本文描述了一种估计植物部分覆盖路径的可穿越性的方法，并在温室中为农业移动机器人导航。传统的移动机器人依赖于仅考虑物体存在的场景识别方法。因此，这些方法无法将柔性植物覆盖的路径识别为可穿越的。在本文中，我们提出了一种新的基于图像语义分割的场景识别框架，用于机器人导航，该框架考虑了覆盖路径的可穿越植物。此外，为了方便地创建遍历性估计模型的训练数据，我们提出了一种基于机器人在数据获取阶段的遍历经验生成图像中可遍历区域标签的方法，我们称之为遍历性掩码。人类通常很难区分图像上可穿越的植物部分。我们的方法能够根据遍历的事实对这些图像区域进行一致且自动的标记。我们进行了一个真实世界的实验，并证实采用所提出的识别方法的机器人在植物丰富的环境中成功导航。