【摘要】：地面三維激光掃描技術(shù)作為新興的測繪數(shù)據(jù)獲取技術(shù),可以快速的獲取高密度、高精度反映物體表面信息的點云數(shù)據(jù),在逆向工程、文物保護、數(shù)字城市、變形監(jiān)測等多個領(lǐng)域得到廣泛應(yīng)用。獲取的點云數(shù)據(jù)大多數(shù)情況下并不具有拓撲信息,需要通過K近鄰來建立點之間的空間關(guān)系。一致定向的法向量場是許多數(shù)據(jù)處理工作的基礎(chǔ),而且定向的法向量能夠提供下墊面的一階逼近和內(nèi)外側(cè)區(qū)分。穩(wěn)健、定向的法向量估算和整個表面的重建工作一樣復(fù)雜。在數(shù)據(jù)獲取過程中,由于儀器自身存在的系統(tǒng)誤差、操作人員水平的高低、外界環(huán)境的干擾,使得掃描獲取的點云數(shù)據(jù)中存在噪聲與離群點,獲取的數(shù)據(jù)不能真實反映掃描對象的幾何信息。目前,主要的點云法向量估算方法是基于主元分析法,主元分析法通過擬合點及其近鄰點的局部總體最小二乘平面,用該局部平面的法向量來近似代替點的法向量。算法對噪聲有一定的抑制作用,但對離群點較為敏感。需要對點云中含離群點時,如何準確的估算法向量進行進一步研究。論文在點云法向量估算現(xiàn)有研究成果的基礎(chǔ)上,針對點云中存在離群點的情況進行研究,主要工作為：1、介紹了地面三維激光掃描技術(shù)的發(fā)展現(xiàn)狀。包括三維激光掃描儀的工作原理、相應(yīng)的處理軟件,介紹了開源C++編程庫PCL(Point Cloud Library)及其基本功能,分析了法向量估算和方向調(diào)整的重點和難點和研究現(xiàn)狀。2、分析了兩種類型的K近鄰,介紹了對散亂點云數(shù)據(jù)建立空間索引的必要性,重點闡述和分析了Kd樹搜索算法的特點,分析兩種點云數(shù)據(jù)格式,并利用PCL提供的Kd樹搜索算法實現(xiàn)了對點云近鄰點的搜索。3、介紹了基于Voroni圖和局部表面擬合的法向量估算,研究了以局部平面擬合為基礎(chǔ)的主元分析法,分析了主元分析法的總體最小二乘本質(zhì),推導(dǎo)了主元分析法的數(shù)學(xué)表達式,并采用主元分析法完成了點云法向量的估算。4、探討存在離群點時,采用主元分析法估算法向量的誤差問題,為了去除離群點,推導(dǎo)了張量投票的閉合解,將點云表示為球張量,采用張量投票算法去除離群點,實驗結(jié)果表明該算法的有效性。
[Abstract]:As a new surveying and mapping data acquisition technology, ground 3D laser scanning technology can quickly obtain point cloud data with high density and high precision to reflect the surface information of objects, in reverse engineering, cultural relics protection, digital cities. Deformation monitoring and other fields have been widely used. In most cases, the obtained point cloud data does not have topological information, so it is necessary to establish the spatial relationship between points through K nearest neighbors. The uniformly oriented normal vector field is the basis of many data processing work, and the oriented normal vector can provide the first order approximation and the inner and outer side discrimination of the underlying surface. Robust, directional normal vector estimation is as complex as the reconstruction of the whole surface. In the process of data acquisition, due to the systematic error of the instrument itself, the level of operators and the interference of the external environment, there is noise and outliers in the point cloud data obtained by scanning. The obtained data can not truly reflect the geometric information of the scanned object. At present, the main point cloud normal vector estimation method is based on the principal component analysis method. The principal component analysis method uses the normal vector of the local plane to approximate the normal vector of the replacement point by fitting the local total least square plane of the point and its nearest neighbor. The algorithm has a certain suppression effect on noise, but it is sensitive to outliers. It is necessary to further study how to estimate the algorithm vector accurately when there are outliers in the point cloud. On the basis of the existing research results of point cloud vector estimation, this paper studies the existence of outliers in point cloud. The main work is as follows: 1. The development status of ground 3D laser scanning technology is introduced. Including the working principle of 3D laser scanner and the corresponding processing software, this paper introduces the open source C programming library PCL (Point Cloud Library) and its basic functions, and analyzes the key and difficult points and research status of normal vector estimation and direction adjustment. This paper analyzes two types of K nearest neighbors, introduces the necessity of establishing spatial index for scattered point cloud data, focuses on the characteristics of Kd tree search algorithm, and analyzes two kinds of point cloud data formats. The Kd tree search algorithm provided by PCL is used to search the nearest neighbor points of point cloud. 3. The normal vector estimation based on Voroni graph and local surface fitting is introduced, and the principal component analysis method based on local plane fitting is studied. In this paper, the essence of the total least square of principal component analysis is analyzed, the mathematical expression of principal component analysis is deduced, and the estimation of point cloud normal vector is completed by principal component analysis. 4, it is discussed that there are outliers. The principal component analysis (PCA) method is used to estimate the error of the algorithm vector. In order to remove the outliers, the closed solution of Zhang Liang's voting is derived, the point cloud is expressed as a spherical tensor, and the Zhang Liang voting algorithm is used to remove the outliers. The experimental results show the effectiveness of the algorithm.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN249

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