| Urban3D model is an important part of "Digital City" that not only offers a virtual simulation for urban planners in urban planning, facilities construction, public security and commonweal services, but also serves as a convenient platform for public participation. Mobile Laser Scanning System is an innovative urban3D modeling technology developed recently that is often used to construct more realistic urban3D models. This dissertation focuses on issues related to processes, difficulties and methods of urban model construction based on Mobile Laser Scanning data. The contents of this research consist of the following parts:Literature reviews and summaries of development of Mobile Laser Scanning System, data features and data processing technology. Mobile Laser Scanning System develops based on traditional radar system and becomes a new measuring system that installed on vehicle platform and integrated with multiple sensors. Compared with Airborne Radar Scanning system, the Mobile Laser Scanning System can obtain higher resolution data of building facade. Moreover, compared with Single-Spot Laser Scanning system, the Mobile Laser Scanning System is capable of scanning combined image and information from different scanning spots by navigating along urban roads, which becomes main methodology of current data collection of street view.Point cloud segmentation integrated with different data features:Laser Scanning point cloud data includes3D coordination of object surface, echo intensity and etc. The color information of scanned object can also be obtained by calibration integrated with data that captured by CCD camera. Based on current methodology of point cloud segmentation from topology and spectrum features, this dissertation has addressed methodology based on proximity judgment on Euclidean distance in multiple dimensions while considered both topology and image features as dimension features of point cloud comprehensively. Furthermore, a weigh coefficient has also been introduced to reflect the influence degree of segmentation from different features. Therefore, the creditability of this algorithm in calculating data from different resources has been enhanced.Object classification in complex urban scene:the Vehicle-Born Radar Scanning system can capture the point cloud data of urban street with regular forms (urban ground, inside of building and traffic sign) and irregular forms (parterre, trees and passengers). At first, this research artificially modularizes the data that is captured by Vehicle-Born Radar Scanning system and also includes ground objects. The regulations of different objects in forming and spatial distributing are also summarized. Secondly, based on the segmentation results, point cloud surface has been set as the minimal recognizing unit in classification; the Objective Oriented Design has been utilized to establish point cloud surface class; the attribution and method of such objective has also been introduced. Therefore, the relationship between urban ground objective and point cloud surface attribution has been established. To avoid the disadvantage of previous arbitrary modularization in point cloud classification, a fixable modularization based on fuzzy feature measurement and credibility judgment has also been addressed. The precision of ground object classification in complex urban scene has been improved accordingly.3D urban model quick construction based on OpenGL:the difference between Boundary Representation model and Constructive Solid Geometry model in3D model construction has been analyzed initially. The basic methodology of OpenGL model construction has also been introduced, and the3D model reconstruction has been implemented based on Data Driven methodology as well. In the following phrase, this research has concentrated on Convex Hall Generation method to construct regular building plane and Hough Transformation and Least Squares method to construct the arc features including dome and pillar, concerning the unique characteristics of urban objectives. The texture has been attached to enhance the realistic simulation.The prototype of point cloud data processing system:The achievements of this research have implemented into a prototype of point cloud data processing system to testify those algorithms. The comparison between point cloud segmentation algorithm, point cloud classification algorithm and3D model reconstruction has been employed by utilizing the existing data that includes Airborne Laser Scanning data, Terrestrial Laser Scanning data and Mobile Laser Scanning data. |
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