发表机构
University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种利用结构光3D相机和点云处理的全自动方法，用于测量复杂几何形状生物打印构建体的厚度，在仿真中达到0.025-0.057毫米的平均绝对误差，并已在机器人生物打印装置上成功验证。
AI 中文摘要
生物打印正作为一种组织工程技术兴起，用于替代大面积损伤的常见治疗方法。虽然生物打印构建体（BPCs）的厚度已被证明对细胞成熟和整合至关重要，但文献中缺乏稳健、自动化和定量的方法来测量这些指标。在本文中，我们提出了一种全自动的基于视觉的方法，用于测量具有复杂几何形状的BPCs的厚度。利用结构光3D相机的点云和RGB图像，我们提出的方法执行基于图像的分割，以从RGB图像中 delineate BPCs，并伴随在点云扫描上执行的新颖的基于几何的厚度测量算法。这些算法将分割掩模与机器人的正向运动学数据和3D点云扫描相结合，以精确测量复杂形状BPCs的上述指标。所提出的方法在仿真和实验研究中进行了评估。在仿真研究中，算法被用于测量一些已知厚度的虚拟创建的BPCs的厚度。测量厚度与真实厚度之间的比较证明了所提出方法的高精度，在空间分辨率为每像素0.1毫米×0.1毫米的仿真中，平均绝对误差在0.025毫米至0.057毫米之间。此外，我们成功地将算法部署在我们的机器人生物打印装置上，该装置利用结构光3D相机，打印了复杂图案，并利用所开发的方法准确测量了打印BPCs的厚度。
英文摘要
Bioprinting is emerging as a tissue engineering technique to replace common treatment methods for large scale injuries. While thickness of the BioPrinted Constructs (BPCs) have shown to be of importance in the cell maturation and integration, the literature lacks a robust, automated, and quantitative method for measuring these metrics. In this paper, we propose a fully automated vision-based method for measuring the thickness of the BPCs with complex geometries. Leveraging the point cloud and RGB images of a structured light 3D camera, our proposed method performs an image-based segmentation for delineating the BPCs from the RGB images, accompanied by novel geometry-based thickness measurement algorithms performed on the point cloud scans. These algorithms combine the segmentation mask with the robot's forward kinematics data and a 3D point cloud scan to precisely measure the aforementioned metrics for complex-shaped BPCs. The proposed method was evaluated in simulation and experimental studies. In simulation studies, the algorithms were used to measure the thickness of some virtually created BPCs with known thickness. The comparison between the measured and true thicknesses demonstrates the high accuracy of the proposed method, achieving mean absolute errors between 0.025 mm and 0.057 mm in simulation at a spatial resolution of 0.1 mm x 0.1 mm per pixel. Furthermore, we successfully deployed the algorithms on our robotic bioprinting setup utilizing a structure light 3D camera, where complex patterns were printed and the developed methods utilized to accurately measure the thickness of printed BPCs.
CommentsThis paper has been accepted for publication at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
