【摘要】：近年來城市三維空間信息的獲取與應用發(fā)展日益成熟,已被廣泛應用于城市建設與規(guī)劃中。車載激光掃描系統(tǒng)可快速、自動、連續(xù)獲取高精度的三維空間數(shù)據(jù),作為一種新的數(shù)據(jù)獲取手段被逐漸應用于地理信息產(chǎn)業(yè)中。車載掃描系統(tǒng)在獲取數(shù)據(jù)時,可近距離獲取城市街區(qū)中多種地物的三維空間信息。而在城市建設中,建筑物貫穿著整個城市,將車載激光掃描系統(tǒng)獲取的建筑物點云數(shù)據(jù)進行快速的分割與提取顯得尤為重要。本文對國內(nèi)外研究現(xiàn)狀進行了總結,介紹了車載掃描系統(tǒng)的構成和工作原理,介紹了點云數(shù)據(jù)的采集過程及點云數(shù)據(jù)的處理流程,總結了其他學者基于激光掃描數(shù)據(jù)所采用的分類方法,通過總結對比選取了本文方法。本文采取基于殘差分析與區(qū)域生長的方法對點云數(shù)據(jù)分別進行了粗分類和細分類。首先本文在粗分類過程運用局部鄰域法向量和基于平面擬合殘差的平整度屬性,將點云分割成不同區(qū)域如建筑物、地表、電桿、植被等。在進行細分類時利用建筑物的平面屬性,將建筑物點云分類成不同的細節(jié)區(qū)域,如窗戶、門、墻體等。在細節(jié)分類中進一步提取建筑的細節(jié)成分時,局部區(qū)域擬合殘差用來決定一個點是否在一個平面區(qū)域內(nèi),法向量夾角來決定鄰域點的相似程度。通過計算兩個參數(shù)θ和St來限制區(qū)域生長過程,在生長過程中能通過St值來剔除噪聲點,以達到分類效果。本文的分類方法在分類過程中不但能將建筑物中的細節(jié)部分進行提取,還能對噪聲點有一定的識別能力,減少內(nèi)存空間。對平面和非平面的點云數(shù)據(jù)有一定的提取效果。
[Abstract]:In recent years, the acquisition and application of urban three-dimensional spatial information has become more and more mature, and has been widely used in urban construction and planning. The vehicle laser scanning system can obtain high precision 3D spatial data quickly, automatically and continuously. As a new means of data acquisition, it has been gradually applied to the geographic information industry. When the vehicle scanning system acquires the data, it can obtain the three-dimensional spatial information of many kinds of ground objects in the city block at close range. In the urban construction, the building runs through the whole city, so it is particularly important to segment and extract the building point cloud data obtained by the vehicle laser scanning system quickly. This paper summarizes the research status at home and abroad, introduces the structure and working principle of the vehicle scanning system, and introduces the acquisition process of point cloud data and the processing flow of point cloud data. The classification methods adopted by other scholars based on laser scanning data are summarized, and the methods in this paper are selected by summary and comparison. In this paper, the rough classification and subdivision of point cloud data are carried out based on residual analysis and regional growth. Firstly, in the rough classification process, the local neighborhood normal vector and the smoothness attribute based on plane fitting residual are used to divide the point cloud into different areas such as buildings, surface, pole, vegetation and so on. The point clouds of buildings are classified into different detail areas, such as windows, doors, walls and so on, by using the plane properties of buildings. When the detail components of the building are further extracted from the detail classification, the local area fitting residual is used to determine whether a point is in a plane region or not, and the angle of the normal vector determines the similarity degree of the neighborhood points. By calculating two parameters theta and St to limit the growth process of the region, the noise points can be eliminated by St value in the process of growth, so as to achieve the classification effect. In the process of classification, the classification method in this paper can not only extract the details of the building, but also have a certain ability to recognize the noise points and reduce the memory space. It has a certain extraction effect on planar and non-planar point cloud data.
【學位授予單位】：昆明理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P225.2

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