KEYWORDS: Point clouds, 3D modeling, Matrices, Data modeling, 3D mask effects, Tunable filters, 3D metrology, Denoising, Neural networks, Computer aided design
With the rapid development of industrial manufacturing, three-dimensional measurement technology has become an effective method to acquire three-dimensional information of industrial products and to conduct defect inspection. When dealing with large-scale target objects like triangular hole panels, it is essential to collect point cloud data from various angles multiple times and register them together to construct a complete three- dimensional model. Prior to registration, preprocessing steps such as denoising the single-frame point cloud data are crucial in enhancing the quality of the point clouds and registration performance. In this paper, a specialized three-dimensional mask for denoising is presented, and the point cloud registration process is divided into two stages: a new coarse registration workflow based on the Rodrigues’ formula and an improved Trimmed ICP algorithm for fine registration. The effects of varied degrees of overlap on the accuracy and speed of point cloud registration are explored to guide data collection. Additionally, contour registration is utilized instead of full point cloud registration to improve efficiency. The experimental results indicate that the process designed in this paper can achieve high-precision point cloud registration quickly with only 25% overlap between two frames, achieving a precision of less than 50μm, which meets the stringent requirements of industrial applications.
KEYWORDS: Point clouds, Structured light, Cameras, Detection and tracking algorithms, 3D projection, Tunable filters, Projection systems, Inspection, Clouds, 3D metrology
The automobile hub plays a crucial role in supporting the weight of the entire vehicle and transmitting power, and the measurement accuracy of critical dimensions is closely related to the safe operation. A high-precision measurement method for critical dimensions of automobile hubs based on the minification projection segmentation algorithm is proposed in this paper. Firstly, the automobile hub point cloud captured by the structured light camera is preprocessed and the surface point cloud is extracted. Then, the cover end point cloud and the bolt hole point cloud are separated through the minification projection segmentation algorithm to calculate the critical dimensions of the automobile hub. Experimental results show that the detection accuracy of the critical dimensions using the method proposed in this paper can reach 100 microns.
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