Dissertation > Excellent graduate degree dissertation topics show

Research on Data Processing Technology Based on Laser Scanning Point Cloud

Author: MengNa
Tutor: ZhouYiQi
School: Shandong University
Course: Mechanical Manufacturing and Automation
Keywords: Laser scanning Point cloud data Sliced Removing noise points Optimize the amount of correction fairing Point cloud compression Discrete Curvature Method Feature Extraction
CLC: TP391.41
Type: PhD thesis
Year: 2009
Downloads: 1306
Quote: 17
Read: Download Dissertation

Abstract


With the computer technology and the increasing development of manufacturing technology, reverse engineering has been widely used in product innovation and re-design. Geometric shape as a product component of a reverse engineering research focus, based on laser scanning point cloud data processing technology as an important reverse engineering geometric modeling technique in which laser scanning point cloud data preprocessing and modeling as a reverse of the basic elements, At present the technology at home and abroad to flourish. The technology to get the point cloud data processing objects, not build triangular mesh, in dealing with ultra-large point cloud, the point cloud data preprocessing, feature extraction and model reconstruction aspects, showing its unique advantages, Reverse engineering is now becoming a hot research. In this paper, a number of the key issues in the field, combined with Shandong Province Natural Science Fund Project \(1) In order to meet subsequent reverse engineering product development and reconstruction accuracy requirements, this paper completed the laser scanning point cloud data noise mathematical description and classification, formulated to minimize all kinds of noise effectively remove even the data preprocessing processes. Based laser scanning point cloud data characteristics are given noise generation mechanism and point mathematical description, according to the mathematical model, the noise points classification: the systematic measurement errors α (x_i, y_i, z_i), and the system randomly error β (x_i, y_i, z_i) noise caused by point, as well as by the random component of the g ~ S (x_i, y_i, z_i) noise caused by point. Respectively, according to its characteristics to develop practical denoising scheme targeted removal. To this end, developed a set of data preprocessing noise removal process, including significant noise point removal, smoothing filtering noise points, point cloud data smoothing treatment. Operating results show that the slice point cloud obtained (from the model of the three-dimensional point cloud data into discrete two-dimensional cross-sectional profile to obtain the point cloud data) on the basis, taken pretreatment processes to remove the noise of the measurement error by the system , random error and random components of the system caused by the reduction or even partially eliminate noise points, reverse engineering to meet up product development and reconstruction accuracy. (2) studied the laser scanning point cloud data preprocessing algorithm for proposed to optimize the amount of correction smoothing algorithms. In this paper, laser scanning point cloud data-intensive, large amount of data is not easy to store in the background, to ensure the accuracy of the data streamlined under the premise puts forward a tolerance deviation parameter and angle values ??for point cloud data compression algorithm The algorithm for processing laser scanning point cloud data point is the result of a large noise removal, filtering and optimizing the amount of smoothing correction after processing of massive point cloud data. The compression algorithm is simple and intuitive, according to the tolerance value d_T maximum tolerance value and angle to compress the size of the data, which can best meet the accuracy requirements of appearance and mechanical products. Be able to maximize the retention of the original shape of a little cloud data to improve the accuracy of the data points after compression of massive point cloud data compression has practical value. For the collected laser scanning point cloud and data-intensive characteristics of large, focus on the section line of point cloud data smoothing treatment, this is rather well-known literature including, Eck M., Jaspert R. and GHLiu, YSWang and YFZhang the proposed smoothing algorithm, however, GHLiu and YSWang et al proposed smoothing algorithm, the correction is progressive, the use of constraint optimization function to determine the amount of correction, so that the bad points to get light Shun, the correction threshold no limit; amended in accordance with the direction of the energy function equation points to change the symbol decision, its programming is relatively complex. Thus, this paper presents an optimization correction smoothing algorithm, rough block, naked along the course of processing the sampled data, respectively, by the curvature of a change order differential symbols to identify dead pixels. Dead pixel correction energy function equation directly in accordance with the direction determined by the point-value centroid of a triangle pointing to the positive or negative G to; correction starts from the initial value, and then follow the energy function equation, progressive search, to meet minimum energy algebraic value after the search stops. The proposed amendment to optimize the amount of smoothing algorithm, mainly used for smoothing scattered laser scanning point cloud data, the algorithm is able to meet the curve smoothing of surface reconstruction requirements, can effectively preserve the original curve geometry. Finally, the two-dimensional example of scattered point cloud simulation to verify the applicability and effectiveness of the proposed algorithm. (3) made with discrete curvature section line feature extraction algorithm. Feature extraction is an important step in reverse engineering, which weakened section line feature point is the need to address the key issues. This paper focuses on two-dimensional cross-section line feature point extraction. Retrieve information about the feature points are extracted directly literature, adaptive k-curvature (AKC) function algorithm, the breakpoint extraction, AKC function is used to extract the corners and smooth connections between the feature points; mapping height function (PHF) algorithm, PHF function is used to distinguish from a circular line segment feature point extraction; Liu and Ma proposed by the relative angle plot (RSTM) algorithm for the identification of the contour feature point extraction problem. AKC function algorithm and PHF algorithm can only extract some feature points, its widespread application subject to certain restrictions. After studying the feature points extracted directly above literature correlation algorithm is proposed based on discrete curvature method extracts feature points. The main content of the proposed method comprising: a Gaussian kernel contains the curvature expressions establishment of relevant mathematical model, the choice of a suitable discrete scale factor. According to a local discrete curvature curve extreme points, determine the section line feature point sets, and the integration of feature points. The proposed algorithm is used to accurately obtain a laser scanning point cloud of the original design intent, the original shape to maximize the consistency characteristic element. Successfully completed a key step in the process of inverse modeling. In the example application, the RSTM algorithms and discrete curvature algorithm proposed applications made at the instance output compare, the result is that the proposed feature extraction algorithm for discrete curvature problem, weakening the feature points can be extracted, is not prone to characteristic points The undetected problem, is a suitable and effective algorithm.

Related Dissertations

  1. Research on Automatic Detection Algorithm for Substructure Distress of Highway Pavement Based on SVM,U418.6
  2. ISAR Imaging Simulation of Space Targets and Target Recognition Based on ISAR Images,TN957.52
  3. Research on Feature Extraction and Classification of Pulse Waveform for Cholecystitis and Nephrotic Syndrome Diagnosis,TP391.41
  4. Application of Q-Learning in the Content-Based Image Retrieval Technology,TP391.41
  5. Research on Transductive Support Vector Machine and Its Application in Image Retrieval,TP391.41
  6. Research on Feature Extraction and Classification of Tongue Shape and Tooth-Marked Tongue in TCM Tongue Diagnosis,TP391.41
  7. Research on Visual Measurement for Spacecraft Rendezvous and Approach,TP391.41
  8. Research on the Image Real-Time Acquisition, Storage and Image Processing System,TP391.41
  9. Feature Extraction, Selection and Combination in Lipreading,TP391.41
  10. Multi-currency Notes Technology Research and Implementation,TP391.41
  11. The Research on Paper Currency Classification Method Based on Harr-Like Feature and Minimal Ball Including Samples,TP391.41
  12. Pavement Distress Recognition Based on Image,TP391.41
  13. Research on Visual Detection and Tracking of Mobile Robots,TP242.62
  14. Research on Fusion Algorithm of Hyper Spectral and High Spatial Resolution Remote Sensing Image,TP751
  15. An Approach for Identifying a Plant Resistance Gene Based on the Random Forest,Q943
  16. Characterization and Identification of Maturity of Pig Manure Composting,S141.4
  17. Tobacco Diseases Auto-Recognition Research Based on Image Processing Technology,S435.72
  18. Research on Nondestructive Detection Technology for External Qualities of Papayas Based-on Vision,S667.9
  19. Research on Identification System of Cashmere and Wool Fiber,TS101.921
  20. Research for Infrared Image Target Identification and Tracking Technology,TP391.41
  21. The Compression and Fusion Technique Research of Underwater Target Feature,TN911.7

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
© 2012 www.DissertationTopic.Net  Mobile