Data Based On Structured Light Scanner Registration Key Technology Research

As an advanced manufacturing technique, reverse engineering can be applied to product design, development and innovation. Data processing of the point clouds is one of the key technologies in reverse engineering. It's very significant to study on data registration, because the registration accuracy and the number of 3D data point have vital effect on the quality of 3D model reconstruction.3D point clouds registration in reverse engineering are researched in detail in this thesis, which consists of the coarse registration and fine registration.A rapid method for point clouds registration based on reference points is proposed, which consists of the coarse registration and fine registration. A set of reference points is applied as an assistant utility to measure the object, which is on the surface of the object. The 3D data of the incremental reference points and density point clouds are acquired by a structure light 3D scanner. The advantage of the registration method of incremental point clouds is that the number of corresponding points of the reference points does not be required 3 or above 3 groups between the new measurement and its predecessor, only is required 3 or above 3 groups between the new measurement and before all the times of measurement. This measurement method is very flexible.In coarse registration, the transformation parameters are estimated by using the reference points only. First, the characteristic of the relative distance between arbitrary two points in the locating points set is used in finding at least three corresponding points. Then, quaternion method is utilized to estimate the transformation parameters. A new coarse registration algorithm based on the reference points is presented and realized.In fine registration, taking the coarse registration results as the initial value, the improved Interactive Closest Point (ICP) algorithm is used in fine registration. The original corresponding points are established rapidly by using the k-d tree searching algorithm. Finally, Preview Model Parameters Evaluation Random Sample Consensus (PERANSAC) algorithm is utilized to remove outliers. The experimental result shows that this method in finding original corresponding points can greatly improve the computation efficiency and also improve the registration accuracy.