本文關(guān)鍵詞： 機(jī)載激光雷達(dá) 濾波處理 建筑物檢測(cè) 屋頂輪廓線提取　出處：《遼寧工程技術(shù)大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：建筑物是城市的重要組成部分,隨著近年來“數(shù)字城市”的不斷升溫,對(duì)于城市三維模型,尤其是建筑物三維模型的需求越來越大。傳統(tǒng)的城市三維模型獲取手段以航空攝影測(cè)量為主,該技術(shù)已十分成熟,但在三維模型重建方面存在較大缺陷,比如地物幾何信息的缺失、三維重建高程精度低等,這主要是由于航空影像數(shù)據(jù)是二維數(shù)據(jù),地面控制點(diǎn)的選取、立體像對(duì)的匹配等都會(huì)造成一定的精度損失。機(jī)載激光雷達(dá)(Airborne LiDAR)作為近幾年發(fā)展迅猛的新型主動(dòng)傳感器,集定姿定位系統(tǒng)(POS)和激光測(cè)距儀為一體,直接獲取地表地物的高密度離散三維點(diǎn)云數(shù)據(jù),為數(shù)字城市建設(shè),特別是建筑物重建提供極大的便利。本文采用原始激光LiDAR點(diǎn)云數(shù)據(jù),在分析建筑物檢測(cè)和屋頂輪廓線提取現(xiàn)有算法的基礎(chǔ)上,主要研究以下幾個(gè)內(nèi)容：(1)LiDAR點(diǎn)云數(shù)據(jù)的濾波處理。本文采用一種移動(dòng)窗口的濾波算法,結(jié)合區(qū)域增長策略對(duì)地面點(diǎn)進(jìn)行檢測(cè),針對(duì)陡坎等地物點(diǎn),將其利用高差閾值歸為地面點(diǎn),實(shí)驗(yàn)證明,該方法對(duì)于地形起伏不大的城區(qū)有較好的處理效果,同時(shí)避免了與建筑物有相似階躍特征的地下地物對(duì)建筑物提取可能存在的干擾。(2)建筑物點(diǎn)云提取算法。本文采用一種基于曲率變化、并結(jié)合共面判斷、設(shè)定高差閾值和區(qū)域增長的建筑物點(diǎn)云提取算法,較為新穎地融合建筑物內(nèi)部點(diǎn)信息和建筑物輪廓邊緣信息,實(shí)驗(yàn)證明,可準(zhǔn)確進(jìn)行建筑物點(diǎn)云提取,并能有效提取曲面屋頂點(diǎn)云數(shù)據(jù)。(3)提出一種直接基于機(jī)載LiDAR點(diǎn)云數(shù)據(jù)的屋頂輪廓線提取算法,根據(jù)相鄰邊緣點(diǎn)間線段某側(cè)不存在激光點(diǎn)的搜索策略,依次檢測(cè)單個(gè)屋頂?shù)倪吘夵c(diǎn),利用最小二乘方法擬合屋頂輪廓線,并對(duì)其進(jìn)行規(guī)則化和擴(kuò)展處理。實(shí)驗(yàn)表明該算法能快速準(zhǔn)確地提取建筑物屋頂輪廓線。
[Abstract]:The building is an important part of the city, with the "digital city" heating up in recent years, for the three-dimensional model of the city, In particular, the demand for 3D models of buildings is increasing. The traditional methods of obtaining 3D models of cities are mainly aerial photogrammetry. The technology is very mature, but there are some defects in the reconstruction of 3D models. For example, the lack of geometric information of ground objects, the low accuracy of 3D reconstruction elevation, etc., which is mainly due to the fact that the aerial image data are two-dimensional data and the selection of ground control points. Airborne LiDAR (Airborne LiDAR), as a new type of active sensor with rapid development in recent years, integrates POS and laser rangefinder. Direct acquisition of high density discrete 3D point cloud data of surface features provides great convenience for digital city construction, especially for building reconstruction. The original laser LiDAR point cloud data are used in this paper. On the basis of analyzing the existing algorithms of building detection and roof contour extraction, this paper mainly studies the filtering processing of point cloud data from the following parts: 1 / 1 / 1 LiDAR. In this paper, a moving window filtering algorithm is used. Combined with the regional growth strategy, the ground points are detected, and the threshold of height difference is classified as the ground points. The experimental results show that the method has a better treatment effect for the urban areas with little topographic fluctuation. At the same time, it avoids the possible interference of underground ground objects with similar step characteristics to the extraction of buildings. The algorithm of point cloud extraction for buildings is based on curvature change and coplanar judgment. The building point cloud extraction algorithm, which sets the threshold of height difference and area growth, integrates the building interior point information with the building contour edge information. The experimental results show that the method can extract the building point cloud accurately. An algorithm for extracting roof contours directly based on airborne LiDAR point cloud data is proposed. According to the search strategy of laser points in a line segment between adjacent edge points, there is no laser point in the line segment between adjacent edge points. The edge points of a single roof are detected in turn, and the roof contour is fitted by the least square method, which is regularized and extended. The experimental results show that the algorithm can extract the building roof contour quickly and accurately.
【學(xué)位授予單位】：遼寧工程技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TU198;TN958.98

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