Robotic practices on the construction site emerge as an attention-attracting manner owing to their capability of tackling complex challenges, especially in the rebar-involved scenarios. Most of existing products and research are mainly focused on the collection of large amounts of data with model training demands. To fulfill this gap, we propose OpenTie, a 3D training-free rebar tying framework utilizing a RGB-to-point-cloud generation and an open-vocabulary rebar detection on the real-world test. We implement the OpenTie via a robotic arm with a binocular camera and guarantee a high accuracy by applying the prompt-based object detection method on the image filtered by our proposed post-processing procedure for the image-to-point-cloud generation framework. Our pipeline requires no training efforts and outperforms the training-based object detection, i.e., YOLO-based method, with the verification on the real-world sequential rebar tying test. The system is flexible for horizontal and vertical rebar tying tasks and holds the potential application to the real construction site with possibility of commercialization.

翻译：机器人技术在建筑工地的实践因其处理复杂挑战的能力而备受关注，尤其是在涉及钢筋的场景中。现有的大多数产品和研究主要集中于收集大量数据以满足模型训练需求。为填补这一空白，我们提出了OpenTie，一种无需三维训练的钢筋绑扎框架，该框架利用RGB到点云生成技术以及面向真实世界测试的开放词汇钢筋检测方法。我们通过配备双目摄像头的机械臂实现了OpenTie，并采用基于提示的目标检测方法对经我们提出的图像到点云生成框架后处理流程过滤后的图像进行处理，从而保证了高精度。我们的流程无需任何训练工作，并在真实世界钢筋连续绑扎测试中验证了其优于基于训练的目标检测方法（如基于YOLO的方法）。该系统灵活适用于水平和垂直钢筋绑扎任务，并具备在真实建筑工地应用的潜力及商业化可能性。