A point cloud registration algorithm for the fusion of virtual and real maintainability test prototypes

In the virtual-real fusion maintainability test scenario, the point cloud registration of maintenance equipment should meet the online real-time requirements and accuracy requirements, so as to provide a good foundation for virtual-real fusion of equipment. To this end, this paper deeply studies the point cloud registration algorithm based on virtual and real fusion. Firstly, the RGBD depth camera is used to collect depth images and color images of objects (spatial and temporal filtering to remove noise), convert it into point cloud data through the camera’s internal parameters, sampling the CAD (computer aided design) model of the test prototype as a model point cloud. Combined with the internal shape descriptor algorithm (ISS) to collect the feature points of the physical point cloud and the CAD model point cloud, using OpenMP multi-core and multi-thread parallel to accelerate the calculation of normal and calculate the unique shape context (USC) descriptor; Then, sample consensus initial aligment algorithm (SAC-IA) is used to coarsely register the virtual and real point clouds to obtain the initial pose; Finally, iterative nearest point (ICP) algorithm based on point-to-surface is used for precise registration. The experimental results prove that the algorithm of this paper has high efficiency, the point cloud coincidence degree after registration is high, and the error is reduced by nearly one order of magnitude compared with other algorithms, which can provide better algorithm support for virtual and real maintenance field.