The regular inspection of sewer systems is essential to assess the level of degradation and to plan maintenance work. Currently, human inspectors must walk through sewers and use their sense of touch to inspect the roughness of the floor and check for cracks. The sense of touch is used since the floor is often covered by (waste) water and biofilm, which renders visual inspection very challenging. In this paper, we demonstrate a robotic inspection system which evaluates concrete deterioration using tactile interaction. We deployed the quadruped robot ANYmal in the sewers of Zurich and commanded it using shared autonomy for several such missions. The inspection itself is realized via a well-defined scratching motion using one of the limbs on the sewer floor. Inertial and force/torque sensors embedded within specially designed feet captured the resulting vibrations. A pre-trained support vector machine is evaluated to assess the state of the concrete. The results of the classification are then displayed in a 3D map recorded by the robot for easy visualization and assessment. To train the SVM we recorded 625 samples with ground truth labels provided by professional sewer inspectors. We make this dataset publicly available. We achieved deterioration level estimates within three classes of more than 92% accuracy. During the four deployment missions, we covered a total distance of 300 m and acquired 130 inspection samples.

中文翻译：

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使用腿式机器人自动检查下水道中的混凝土劣化

定期检查下水道系统对于评估退化程度和规划维护工作至关重要。目前，人工检查员必须穿过下水道并使用他们的触觉来检查地板的粗糙度并检查是否有裂缝。使用触觉是因为地板经常被（废水）和生物膜覆盖，这使得目视检查非常具有挑战性。在本文中，我们展示了一种机器人检测系统，该系统使用触觉交互评估混凝土的劣化。我们在苏黎世的下水道中部署了四足机器人 ANYmal，并使用共享自主权命令它执行多项此类任务。检查本身是通过使用下水道地板上的一个肢体进行明确定义的刮擦动作来实现的。嵌入在专门设计的脚中的惯性和力/扭矩传感器捕获由此产生的振动。评估预训练的支持向量机以评估混凝土的状态。然后将分类结果显示在机器人记录的 3D 地图中，以便于可视化和评估。为了训练 SVM，我们记录了 625 个带有由专业下水道检查员提供的真实标签的样本。我们公开提供此数据集。我们在三个类别中实现了超过 92% 的准确度的恶化水平估计。在四次部署任务中，我们总共覆盖了300 m，并获得了130个检查样本。然后将分类结果显示在机器人记录的 3D 地图中，以便于可视化和评估。为了训练 SVM，我们记录了 625 个带有由专业下水道检查员提供的真实标签的样本。我们公开提供此数据集。我们在三个类别中实现了超过 92% 的准确度的恶化水平估计。在四次部署任务中，我们总共覆盖了300 m，并获得了130个检查样本。然后将分类结果显示在机器人记录的 3D 地图中，以便于可视化和评估。为了训练 SVM，我们记录了 625 个带有由专业下水道检查员提供的真实标签的样本。我们公开提供此数据集。我们在三个类别中实现了超过 92% 的准确度的恶化水平估计。在四次部署任务中，我们总共覆盖了300 m，并获得了130个检查样本。