A 3-D Reconstruction Method for Large Freeform Surfaces Based on Mobile Robotic Measurement and Global Optimization
Jinshan Wang, Zeyu Gong, Bo Tao, Zhouping Yin
Abstract
Automatic measurement and 3-D reconstruction are critical to the robotic manufacturing of large surface parts. The existing approaches have limitations in terms of measurement range, automation degree, and reconstruction accuracy. To achieve the automatic measurement and reconstruction of large freeform surface parts with high accuracy and high efficiency, this article proposes a measurement approach based on a mobile industrial robot and a 3-D reconstruction method based on multi-viewpoint cloud registration. First, to perform the automatic measurement and obtain the locally measured point clouds of the large freeform surface part, a mobile measurement robot is employed. And the initial global measurement poses, estimated by the robot’s pose information and the light detection and rangings (LiDAR’s) location result, are used to coarsely stitch the local multi-viewpoint clouds. Second, an adjacency judgment approach based on point cloud collision detection is proposed, with which the local measurement pose constraints are constructed. Finally, a global optimization approach based on improved pose graph optimization is proposed to achieve accurate global measurement poses estimation and high-accuracy reconstruction of multi-viewpoint clouds. Experiments were carried out on measuring and reconstructing a standard test blade and a real wind turbine blade. The results demonstrate that the proposed approach reaches the mean accuracy of 0.835 mm for a measuring range of about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.5\times2.5\,\,\text{m}^{2}$ </tex-math></inline-formula> , which outperforms the previous methods in terms of measuring range, accuracy, and practicality.