Accurate robotic manipulation of test tubes in biology and medical industries is becoming increasingly important to address workforce shortages and improve worker safety. The detection and localization of test tubes are essential for the robots to successfully manipulate test tubes. In this paper, we present a framework to detect and estimate poses for the in-rack test tubes using color and depth data. The methodology involves the utilization of a YOLO object detector to effectively classify and localize both the test tubes and the tube racks within the provided image data. Subsequently, the pose of the tube rack is estimated through point cloud registration techniques. During the process of estimating the poses of the test tubes, we capitalize on constraints derived from the arrangement of rack slots. By employing an optimization-based algorithm, we effectively evaluate and refine the pose of the test tubes. This strategic approach ensures the robustness of pose estimation, even when confronted with noisy and incomplete point cloud data.

准确的机器人操作生物学和医学领域的试管对于解决劳动力短缺和提高工人安全性变得越来越重要。本文提出了一种框架，利用颜色和深度数据来检测和估计试管架中试管的姿态。该方法包括使用 YOLO 目标检测器对提供的图像数据中的试管和试管架进行有效分类和定位。随后，通过点云配准技术估计试管架的姿态。在估计试管姿态的过程中，我们利用了试管架插槽布置导出的约束条件。通过使用基于优化的算法，我们有效地评估和优化试管的姿态。这种策略性的方法确保了在面对嘈杂和不完整的点云数据时姿态估计的稳健性。

使用RGB-D数据的机架内试管姿态估计