US 12,406,382 B2
Method for virtual pre-assembly matching of prefabricated beams based on design-measured point cloud models
Wen Xiong, Nanjing (CN); Chang Xu, Nanjing (CN); and Yanjie Zhu, Nanjing (CN)
Assigned to SOUTHEAST UNIVERSITY, Jiangsu (CN)
Appl. No. 18/256,266
Filed by Southeast University, Jiangsu (CN)
PCT Filed Dec. 28, 2022, PCT No. PCT/CN2022/142697
§ 371(c)(1), (2) Date Jun. 7, 2023,
PCT Pub. No. WO2023/226429, PCT Pub. Date Nov. 30, 2023.
Claims priority of application No. 202210570841.0 (CN), filed on May 24, 2022.
Prior Publication US 2025/0218008 A1, Jul. 3, 2025
Int. Cl. G06T 7/33 (2017.01); G06T 3/14 (2024.01); G06T 5/70 (2024.01)
CPC G06T 7/344 (2017.01) [G06T 3/14 (2024.01); G06T 5/70 (2024.01); G06T 2207/10028 (2013.01)] 5 Claims
OG exemplary drawing
 
1. A method for virtual pre-assembly matching of prefabricated beams based on design-measured point cloud models, comprising the following steps:
step (1): computing an oriented bounding box respectively for 3D point clouds of two prefabricated beams with assembly relationship therebetween, and implementing 3D coordinate calibration based on geometric features of the 3D point clouds of the two prefabricated beams; for the calibrated 3D point clouds of the two prefabricated beams, making two point cloud slices at an assembly interface of the two prefabricated beams respectively; and based on an assembly interface jointly contained in the two point cloud slices, generating a discrete design point cloud for design information contained in the assembly interface;
step (2): registering the point cloud slices at the assembly interface of the two prefabricated beams in step (1) with the generated design point cloud respectively by an iterative closest point algorithm; and setting a distance threshold on the basis of a coordinate range of the design point cloud to denoise the two point cloud slices at the assembly interface;
step (3): based on the coordinate range of the design point cloud in step (2), partitioning the two denoised point cloud slices; and selecting a fitting function and a fitting algorithm according to features required to be fitted, to fit and extract boundary features and corner features of an assembly interface of two components to be assembled; and
step (4): based on the fitted boundary features and corner features of the pre-assembly interface of the two components to be assembled in step (3), implementing coarse matching of the assembly interface by a Procrustes analysis algorithm firstly, and then implementing fine matching of the assembly interface by the iterative closest point algorithm, adjusting the 3D point cloud to be in a final assembly posture, computing a matching degree error of the assembly interface, and evaluating an assembly result.