【摘要】：本文以面向對象的點云分析理論為指導,開展車載激光雷達(MLS)點云數據處理和信息提取工作,主要圍繞建筑物立面識別、建筑物立面重建兩個主題開展相關研究。主要研究內容包括以下四點：(1)車載激光點云分割與合并本文采用法向量與距離約束進行點云分割,該方法的穩(wěn)健性好,但是分割后存在過分割現象。為此,采用kd樹來確定分割面片的臨近關系,采用分割面片之間的法線向量角度差異與分割面片之間的距離約束作為合并準則,進行分割面片合并。(2)基于先驗知識的建筑物立面識別與提取點云分類和目標識別方面通常使用的特征包括：尺寸、形狀、位置、方向、色彩、拓撲關系等特征。鑒于建筑物立面通常垂直于水平面、面積較大、顯著高于周邊地物這三個特點,本文主要使用分割面片的法線向量與水平面夾角、面積、絕對高程和高程變化范圍三個特征提取建筑物立面面片。(3)車載激光點云建筑物立面輪廊線提取由于車載激光掃描點云數據中存在大量的立面點,傳統(tǒng)的基于不規(guī)則三角網的機載激光掃描數據輪廓線提取方法失去了效力。本文對該傳統(tǒng)方法進行改進,以適用于車載激光掃描數據。(4)建筑物立面拓撲關系矯正由于常規(guī)的特征值法提取的建筑物立面信息不準確。為此,基于建筑物立面垂直于水平面、建筑物立面之間存在垂直或者平行關系等先驗知識,根據建筑物立面的空間法線向量檢測建筑物立面之間的拓撲關系,并對建筑物立面之間的拓撲關系進行校正。面向對象的MLS點云建筑物立面識別、建筑物立面重建具有優(yōu)異的效果,穩(wěn)健性高。并且可以適用于復雜場景區(qū)域的點云數據處理和分析,為MLS點云數據建筑物立面識別與重建提供了一種行之有效的方法。
[Abstract]:Guided by the theory of object-oriented point cloud analysis, this paper carries out the data processing and information extraction of (MLS) point cloud of vehicle-mounted lidar, mainly focusing on the two topics of building elevation identification and building facade reconstruction. The main research contents are as follows: (1) vehicle laser point cloud segmentation and merging this paper uses normal vector and distance constraint to segment point cloud. This method has good robustness, but there is an over-segmentation phenomenon after segmentation. Therefore, the kd tree is used to determine the proximity relationship of the segmented patches, and the distance constraint between the normal vector angles and the segmented patches is adopted as the merging criterion. (2) recognition and extraction of building elevation based on prior knowledge. (2) the features commonly used in point cloud classification and target recognition include size, shape, position, direction, color, topological relation and so on. Since the facade of a building is usually perpendicular to the horizontal plane and has a larger area, which is significantly higher than the surrounding features, this paper mainly uses the normal vector of the segmented surface and the angle and area between the horizontal plane and the normal vector. Three features, absolute elevation and range of elevation variation, are used to extract facade patches of buildings. (3) because there are a large number of vertical points in vehicle laser scanning point cloud data, there are a lot of vertical points in vehicle laser spot cloud elevation veranda extraction of building facade. The traditional method of airborne laser scanning data contour extraction based on irregular triangulation has lost its effectiveness. In this paper, the traditional method is improved to be suitable for vehicle-borne laser scanning data. (4) the topology relation of building elevation is corrected because of the inaccuracy of building elevation information extracted by the conventional eigenvalue method. Therefore, based on the prior knowledge that the building facade is perpendicular to the horizontal plane and there is a vertical or parallel relationship between the building facades, the topological relationship between the building facades is detected according to the spatial normal vector of the building facade. The topological relationship between the facades of buildings is corrected. Object-oriented MLS point cloud building facade recognition, building facade reconstruction has excellent effect, high robustness. And it can be applied to point cloud data processing and analysis in complex scene area, which provides an effective method for building elevation recognition and reconstruction based on MLS point cloud data.
【學位授予單位】：遼寧工程技術大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TU198

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本文編號：2446345