Robotic tiling is an efficient way to replace manual work, with tile detection and positioning serving as a pivotal technology. However, the tiling environment is characterized by its complexity. This paper introduces the instance segmentation method Mask R-CNN, which can detect tiles in non-structural environments after proper training. To address the difficulty of acquiring datasets and high annotation costs, a densely arranged tile dataset that allows for automatic labeling has been synthesized and various designed data augmentation techniques are employed. The trained model achieves a detection performance with AP75 = 98.94 % and AP95 = 88.14 % on 100 test images. Subsequently, shape reconstruction is performed to estimate 3D poses of tiles using PNP principle. Finally, a tiling system is developed and combining visual detection with laser detection method enables a successful tiling experiment. Results show that the positional error is less than 0.66 mm and the directional error is less than 0.27°, which meets industrial requirements.

中文翻译：

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在非结构环境中使用掩码 R-CNN 进行瓷砖检测，用于机器人瓷砖


机器人平铺是替代手动工作的有效方法，而平铺检测和定位是一项关键技术。但是，平铺环境的特点是其复杂性。本文介绍了实例分割方法 Mask R-CNN，该方法经过适当的训练后可以检测非结构环境中的瓦片。为了解决数据集获取难度大、标注成本高等问题，综合了允许自动标注的密集瓦片数据集，并采用了各种设计的数据增强技术。经过训练的模型在 100 张测试图像上实现了 AP75 = 98.94 % 和 AP95 = 88.14 % 的检测性能。随后，使用 PNP 原理进行形状重建以估计瓷砖的 3D 姿势。最后，开发了一种平铺系统，并将视觉检测与激光检测方法相结合，实现了成功的平铺实验。结果表明，位置误差小于 0.66 mm，方向误差小于 0.27°，满足工业要求。