本文選題：多源LiDAR數(shù)據(jù) + 平面擬合��； 參考：《西南交通大學(xué)》2014年碩士論文

【摘要】：建筑物信息對城市的建設(shè)和規(guī)劃,社會的可持續(xù)發(fā)展有著重大影響,如何快速、有效地獲取城市建筑物三維空間信息成為目前研究的熱點。本文針對數(shù)字城市建設(shè)建筑物三維建模中如何構(gòu)建各個立面的問題,融合地面與機載LiDAR點云數(shù)據(jù),采用全局閾值平面擬合濾波算法,精確地提取出建筑物平面目標,根據(jù)空間幾何信息對建筑物進行質(zhì)量檢測與幾何校準,實現(xiàn)建筑物的三維模型構(gòu)建。具體研究工作如下：(1)研究基于特征線的半自動點云數(shù)據(jù)配準融合方法。重點研究了半自動配準融合方法中的特征線定位,對方法進行改進,利用法向量約束求取平坦度得到平坦度差值,提取特征線。結(jié)合實驗數(shù)據(jù)的特殊性,在地理概略粗定位的基礎(chǔ)上,利用ICP算法拼接特征線,實現(xiàn)半自動數(shù)據(jù)配準融合,通過實驗驗證算法的可行性,對不同特征線下的配準效果進行分析比較。利用地理概略粗定位與特征線精定位的方法能較好地進行地面與機載LiDAR點云數(shù)據(jù)配準融合,配準融合的效果與選取的特征線密切相關(guān)。(2)改進一種利用斜率進行平面擬合的濾波算法。在歸納總結(jié)國內(nèi)外學(xué)者在相關(guān)問題的研究基礎(chǔ)之上,對該算法的閾值處理部分進行改進,針對算法中閾值具有模糊性等問題,預(yù)先對建筑物平面分類,利用總體最小二乘法的思想進行全局閡值估計。通過實驗分析比較算法改進前后的精確度,分析其可行性。采用總體最小二乘法的全局閾值平面擬合濾波算法可以優(yōu)化平面擬合的過程,并能一定程度地提高平面擬合的準確性。(3)實現(xiàn)實驗數(shù)據(jù)的三維重建。選擇具有代表性的實驗數(shù)據(jù),通過對建筑物平面的空間幾何關(guān)系進行分析,對平面的平整度與傾斜度進行計算,在此基礎(chǔ)之上實現(xiàn)平面的空間校準,對校準后的建筑物平面進行三維重建。進行空間幾何關(guān)系校準后的建筑物平面具有精度更高、適應(yīng)性更強的特點,更好地反映了建筑物的細部空間三維信息；融合地面與機載LiDAR點云數(shù)據(jù)的建筑物三維重建能更精細地反映建筑物真實三維信息,但其自動化程度仍有待提高。研究表明,基于特征線的半自動點云數(shù)據(jù)配準方法能較好地融合地面與機載點云數(shù)據(jù),但該方法明顯受所選取特征線的影響；改進的總體最小二乘平面擬合濾波法可以提高平面擬合的準確性；結(jié)合地面與機載點云的建筑物輪廓信息提取能較真實的反映建筑物的三維信息。
[Abstract]:Building information has great influence on the construction and planning of cities and the sustainable development of society. How to obtain 3D spatial information of urban buildings quickly and effectively has become a hot research topic at present. In this paper, aiming at the problem of how to construct each facade in the 3D modeling of digital city building, the point cloud data of ground and airborne LiDAR are fused, and the global threshold plane fitting filtering algorithm is used to extract the building plane target accurately. According to the spatial geometric information, the quality of the building is checked and calibrated, and the 3D model of the building is constructed. The main work is as follows: (1) A semi-automatic point cloud data registration and fusion method based on feature lines is studied. The feature line location in semi-automatic registration fusion method is studied, and the method is improved. The flatness difference is obtained by using normal vector constraints, and the feature line is extracted. Combined with the particularity of the experimental data, on the basis of rough geographical location, the ICP algorithm is used to join the feature lines to realize the semi-automatic data registration fusion, and the feasibility of the algorithm is verified by experiments. The registration effect under different characteristic lines is analyzed and compared. The method of rough geographical location and fine feature line location can be used for the registration and fusion of ground and airborne LiDAR point cloud data. The effect of registration fusion is closely related to the selected feature lines. On the basis of summing up the research on the related problems, this paper improves the threshold processing part of the algorithm, aiming at the fuzzy threshold in the algorithm, classifies the building plane in advance. The global threshold value is estimated by using the idea of total least square method. The accuracy and feasibility of the improved algorithm are analyzed and compared by experiments. The process of plane fitting can be optimized by using the global threshold plane fitting filtering algorithm based on the global least square method, and the accuracy of plane fitting can be improved to a certain extent, and the 3D reconstruction of experimental data can be realized. Select the representative experimental data, through the analysis of the spatial geometric relationship of the building plane, calculate the flatness and inclination of the plane, and realize the spatial calibration of the plane on this basis. The three-dimensional reconstruction of the calibrated building plane is carried out. After the calibration of the spatial geometric relations, the building plane has the characteristics of higher accuracy and stronger adaptability, which better reflects the three-dimensional spatial information of the building. The 3D reconstruction of buildings with ground and airborne LiDAR point cloud data can more accurately reflect the real 3D information of buildings, but the degree of automation still needs to be improved. The research shows that the semi-automatic point cloud registration method based on feature line can fuse the ground and airborne point cloud data well, but this method is obviously affected by the selected feature lines. The improved least square plane fitting filtering method can improve the accuracy of plane fitting, and extract the building contour information from ground and airborne point clouds to reflect the 3D information of buildings.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TU198

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相關(guān)期刊論文 前1條

1 王志哲;余玲玲;楊安康;;基于曲面法向量的曲面ICP拼接算法研究[J];微計算機信息;2010年21期

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本文編號：2030574