發(fā)布時間：2019-07-05 14:44

【摘要】：建筑物是城市中最主要的元素，對城市地區(qū)建筑物進行建模對于數(shù)字城市的建立具有較為重要的意義。機載LiDAR測量技術(shù)的發(fā)展為獲取城市空間信息提供了一種全新的技術(shù)手段，其中機載LiDAR所獲取的點云數(shù)據(jù)中包含有大量的建筑物空間信息，特別是由于機載LiDAR的工作原理，建筑物信息擁有大量的豐富的建筑物屋頂信息。因此機載激光雷達的發(fā)展為數(shù)字城市中建筑物建模需求提供了有力的支持。 本文主要對機載激光雷達的技術(shù)發(fā)展、數(shù)據(jù)的特點、國內(nèi)外發(fā)展狀況及未來的發(fā)展趨勢做出相關(guān)的描述，并提出了關(guān)于建筑物點云數(shù)據(jù)的建筑物提取及重建的理論方法。本文的具體內(nèi)容如下： 一、對原始數(shù)據(jù)的預處理工作，包括誤差點的剔除，濾波和提取建筑物點三個過程，經(jīng)過這幾步為后面的建筑物點預處理做好提前準備工作。 二、對建筑物群點云采用區(qū)域增長的聚類方法將建筑物與建筑物之間分割開，由于剔除非建筑物數(shù)據(jù)點過程存在誤差等原因，并不能將每個建筑物都分割成單獨建筑物，作者對現(xiàn)有射線法進行改進采用人工對錯誤分割的建筑物再次目視判斷分割。 三、對現(xiàn)有的凸殼算法進行了改進，利用改進后算法獲取建筑物點云數(shù)據(jù)的包圍殼點，并將這些點連接并規(guī)則化后生成建筑物的規(guī)則邊界輪廓線。 四、除平頂房屋外，建筑物頂部通常是由多個面構(gòu)成的，構(gòu)建建筑物的簡單模型就需要判定建筑物屋頂點的面片歸屬類別，文中對現(xiàn)有的三角網(wǎng)法向量法進行了改進，對點的面片類別進行分類取得良好的效果。 五、采用最小二乘法，對各建筑物中同屬于一個面片的點云擬合最佳面片方程，求取相交平面的直線方程，結(jié)合規(guī)則輪廓線以及投影理論求建筑物的特征點。 六、本文根據(jù)以上步驟，，對平頂建筑物以及人字頂、四坡頂、L形人字頂?shù)群唵谓ㄖ飿?gòu)建模型，并取得較好效果。
文內(nèi)圖片：
圖片說明：1規(guī)則房屋屋頂類型Fig.1.3.1Thetypeofregularbuildingroofs
[Abstract]:Buildings are the most important elements in the city, so it is of great significance to model the buildings in urban areas for the establishment of digital cities. The development of airborne LiDAR measurement technology provides a new technical means for obtaining urban spatial information. The point cloud data obtained by airborne LiDAR contains a large number of building spatial information, especially because of the working principle of airborne LiDAR, building information has a large number of building roof information. Therefore, the development of airborne lidar provides strong support for building modeling requirements in digital cities. In this paper, the technical development of airborne lidar, the characteristics of data, the development status at home and abroad and the development trend in the future are described, and the theoretical methods of building extraction and reconstruction of building point cloud data are put forward. The specific contents of this paper are as follows: first, the preprocessing of the original data, including the elimination of error, filtering and extraction of building points, after these steps for the post-building point preprocessing to do the preparatory work in advance. Secondly, the clustering method of regional growth is used to divide the building from the building. Because of the error in the process of eliminating the non-building data points, each building can not be divided into separate buildings. The author improves the existing beam method and adopts the artificial visual judgment segmentation of the incorrectly segmented buildings. Thirdly, the existing convex hull algorithm is improved, and the surrounded shell points of building point cloud data are obtained by using the improved algorithm, and these points are connected and standardized to generate the regular boundary outline of the building. Fourth, in addition to the flat roof house, the top of the building is usually composed of multiple faces. It is necessary to determine the classification of the facet at the top of the building. In this paper, the existing triangular net normal vector method is improved, and good results are obtained in the classification of the facet categories of the points. Fifth, the least square method is used to fit the best patch equation for the point cloud which belongs to the same facet in each building, and the straight line equation of the intersecting plane is obtained, and the characteristic points of the building are obtained by combining the regular outline and the projection theory. 6. According to the above steps, this paper constructs models for flat-topped buildings and simple buildings such as herringbone roof, four-slope roof, L-shaped herringbone top and so on, and obtains good results.
【學位授予單位】：遼寧工程技術(shù)大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：P225.1

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本文編號：2510601