本文關(guān)鍵詞： 三維激光 點云數(shù)據(jù) 三維建模 特征提取　出處：《昆明理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：點云數(shù)據(jù)處理與建模是激光掃描系統(tǒng)研究中的一項重要內(nèi)容。對于不同的目標(biāo)物體其建模方式也不近相同。常用的建模方式是在網(wǎng)格構(gòu)建的基礎(chǔ)上進(jìn)行擬合建模,適用于形狀復(fù)雜,表面變化較大的物體。但網(wǎng)格的構(gòu)建計算量大,該建模方法常用于數(shù)據(jù)量較小的物體。對于建筑物而言,數(shù)據(jù)量大,通常形狀較為規(guī)則,需要借助專業(yè)的建筑物建模軟件,可以直接將點云數(shù)據(jù)導(dǎo)入到建模軟件中,然后手動選擇點方式來實現(xiàn)模型構(gòu)建,但是點云數(shù)據(jù)量大,選點是完全人工的過程,模型構(gòu)建結(jié)果的好壞與建模人員的操作熟練程度直接相關(guān),建模精度得不到保證。而基于特征點的建模方式,是在特征提取的基礎(chǔ)上建模,這種建模方法自動化程度得到提高,使建模精度得到一定的保證,并且可以減少人工操作失誤造成的精度損失問題。本文闡述了地面三維激光掃描儀的工作原理,對作業(yè)中所產(chǎn)生誤差分析,討論減少誤差的方法。對點云數(shù)據(jù)的預(yù)處理,包括點云配準(zhǔn)、點云去噪、點云精簡、數(shù)據(jù)分割等不同處理方法進(jìn)行了論述,并對每一種方法的優(yōu)缺點以及適用性做了探討。利用Geomagic Studio軟件對原始點云數(shù)據(jù)進(jìn)行去噪、數(shù)據(jù)精簡等處理。闡釋了曲率估計、主成分分析特征提取方法,并利用Matlab編程實現(xiàn)主成分分析法對點云數(shù)據(jù)特征點提取,利用Imageware軟件通過曲率估算對點云數(shù)據(jù)特征提取,再通過人工手動修改完善特征點提取。最后將點云導(dǎo)入3DMax中,利用手動選點的方式直接對點云數(shù)據(jù)進(jìn)行建模,再將點云數(shù)據(jù)導(dǎo)入Sktechup中,對點云數(shù)據(jù)特征提取的同時完成建模。最終利用Geomagic Control將兩種方式所建模型分別與原始點云數(shù)據(jù)作對比,生成呈標(biāo)準(zhǔn)分布的標(biāo)準(zhǔn)偏差統(tǒng)計圖,偏差值都是集中在一定范圍內(nèi),說明兩種建模方法都是可行的。從3D結(jié)果對比表中可看出,基于特征提取建模方式的四種偏差值均比基于手動選點建模方式的小,該實驗證明基于特征提取的方法建模能夠有效的減少誤差,建模精度更好,能有效的提高建模效率。
[Abstract]:Point cloud data processing and modeling is an important part of laser scanning system. The modeling methods for different target objects are not nearly the same. The commonly used modeling methods are fitting modeling based on mesh construction. . It is suitable for objects with complex shapes and large surface changes. However, the modeling method is often used for objects with small amount of data. For buildings, the amount of data is large, but the shape is usually more regular. With the help of professional building modeling software, point cloud data can be directly imported into the modeling software, and then manually select points to achieve model building, but point cloud data is large, point selection is a completely artificial process. The result of modeling is directly related to the proficiency of the modeler, and the precision of modeling is not guaranteed. However, the modeling method based on feature points is based on feature extraction. The degree of automation of this modeling method is improved, and the precision of modeling is guaranteed to a certain extent. And it can reduce the loss of precision caused by manual error. This paper describes the working principle of the 3D laser scanner on the ground, and analyzes the error generated in the operation. The preprocessing of point cloud data, including point cloud registration, point cloud denoising, point cloud reduction, data segmentation and other different processing methods are discussed. The advantages and disadvantages and applicability of each method are discussed. The original point cloud data is de-noised and reduced by Geomagic Studio software, and curvature estimation is explained. Principal component analysis (PCA) is used to extract feature points from point cloud data by Matlab programming. Imageware software is used to extract the feature of point cloud data by curvature estimation, and then manually modify and improve the feature point extraction. Finally, point cloud is imported into 3DMax. The point cloud data is modeled directly by manually selecting points, and then the point cloud data is imported into Sktechup. Finally, we use Geomagic Control to compare the two models with the original point cloud data. The results show that the two modeling methods are feasible, which can be seen from the contrast table of 3D results. The four deviations based on feature extraction are smaller than those based on manual selection. This experiment proves that the method based on feature extraction can effectively reduce the error and the modeling accuracy is better. Can effectively improve the efficiency of modeling.
【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】：2017
【分類號】：P225.2

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本文編號：1472085