Research On Data Processing Technology Based On Laser Scanning Point Cloud

With the development of computer and mechanical manufacturing technology, reverse engineering has been widely used in product re-innovation and design. As a research hot topic of geometrical components and an important geometric modeling technique in reverse engineering, data processing technology based on laser scanning point cloud, which is regarded scattered point cloud data as the the basic element during the process of data pre-processing and modeling, and which is so important that it is full of development at home and abroad at present time. The data processing technology, regarded the obtained point cloud data as processing objects and without building a triangular mesh, now shows its unique advantages and is becoming a hot research spot, during the process of dealing with very large scale point cloud, point cloud data preprocessing, feature extraction and model reconstruction. In this paper, some key issues in the field of reverse engineering have been developed deeply, which is helped by the National Natural Science Foundation of Shandong Province " Complicated Surface Reconstruction Technology Research Based on Multi-scale Features " (Item Number: Y2006F12).(1) In order to meet the precision of following product development and reconstruction in reverse-engineering, mathematical description and classification about noise have been completely finished, and a set of data pre-processing process has been set up, which can minimize or effectively eliminate the noise.According to the characteristics of laser scanning point cloud data, some mathematical description and generated mechanism of noise points are descriped. Based on the established mathematical model, the noise points are classified into two categories, one is that caused by the system measurement errorα(x_i,y_i,z_i)and the system random errorβ(x_i,y_i,z_i), the other is that caused by the random component g~s(x_i,y_i,z_i), the removal of which is carried up according to their characteristics relied on some feasible de-noised methods. So, a set of de-noised process of data pre-processing is developed, including the removal of obviously noise points, smoothing filter process of noise points, and smoothing process of point cloud data, etc.. The run results show that the de-noised pre-processing process can minimize noise points, and can meet the precision of following product development and reconstruction in reverse-engineering based on the sliced point cloud data (from three-dimensional discrete point cloud data of the model to the obtained point cloud data of two-dimensional cross-section contour), which caused by system measurement error, system random error and system random component.(2) The preprocessing algorithms about massive laser scanning point cloud data have been studied, and an optimized amount of smoothing algorithm has been proposed.In this paper, an bias parameter algorithm and an allowable difference of angle is presented to compress point cloud data, under the condition of intensive and massive laser scanning point cloud data which is not easy to store, and data accuracy assured after data compression, which is used to process laser scanning point cloud data through the big noise points removal, filtering and optimal amount smoothing process. The above mentioned compression algorithm is simple and intuitive, which can compress the data based on the size of the tolerance values about bias parameter and bias angle, and can meet the required appearance and precision of mechanical products greatly. The algorithm can preserve the original shape of point cloud data, and can improve the accuracy of the compressed data points, which has practical application value on the compression of massive point cloud data.As the collected laser scanning point cloud data is intensive and large, some smoothing process algorithm about laser scanning point data of a sectional curve have been mainly researched. The well-known literatures in this field, Eck M., Jaspert R., and G.H. Liu, Y.S. Wang and Y.F.Zhang, in which an smoothing algorithm is proposed. However, the smoothing algorithms are proposed by G.H.Liu and Y.S.Wang etc., the revised amount is progressive, and the constrained optimizing function is used to determine the amendment amount, which has no limit to the threshold value. And the revised direction to point is determined in accordance with changes of energy function equation symbols, in which the its program is relatively complex. Thus, an optimized amount of smoothing algorithm is presented in this paper, which identified bad points in accordance with the sign change of their curvatures and corresponding first-order differences during the process of coarse smoothing and fine smoothing. The corrected direction of bad points is pointed to the positive or negative G of the triangle centroid from the sampled data in accordance with the energy function equation. The revised amount has been an incremental search, beginning with an initial value and then following the energy function equation, until the minimum value of energy algebraic expression is meet. The proposed optimized amount smoothing algorithm in this paper, mainly used for smoothing scattered laser scanning point cloud data, can meet the smoothness requirements of curve and surface reconstruction. The proposed algorithm is particularly effective in terms of shape preservation. Case studies are presented that illustrate the efficacy of the proposed algorithm.(3) A discrete curvature algorithm is proposed to extract feature points of a sectional curve.Feature extraction is an important process in reverse engineering, in which weak feature points of a sectional curve is the key issues to be dealed with. The feature points extraction of two-dimensional cross-sectional curve is focused on in this paper. The retrieved literatures about feature points extracted directly include the adaptive k-curvature (AKC) function algorithm, which is used to extract the feature points between corner and smooth connection, the mapping height function (PHF) algorithm, which is used to distinguish feature points from arc and line segments, and the relative angle mapping (RSTM) algorithm proposed by Liu and Ma, which is used to identify the feature points of contours. The AKC function algorithm and PHF algorithm can only extract some certain feature points, which has certain restrictions used in the wide field.Based on directly extracting feature points of the above-mentioned documents, the discrete curvature method is proposed to extract feature points. The main contents of the proposed algorithm include, the curvature expression comprised of Gaussian kernel function curve is used to establish the relevant mathematical models, and a suitable discrete scale factor is chosen. According to the local extreme points of discrete curve, a set of feature points is determined, and the fuse of feature points is carried out subsequently. The proposed algorithm is used to accurately obtain the original design intent of the laser scanning point cloud, which can greatly keep with the original shape feature cell consistently. Then a key step in reverse modeling process is successfully completed. During the course of example application, an output comparison between the RSTM algorithm and the proposed discrete curvature algorithm has been carried out, which the the result is that the proposed method in this paper can extract the weak feature points, and cannot prone to undetect feature points. Then the output results show the proposed algorithm is practical and effective.