本文關(guān)鍵詞：基于地面LiDAR數(shù)據(jù)的建筑物立面提取及建模研究　出處：《東華理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 三維激光掃描技術(shù) RANSAC算法 建筑物立面提取 模型重建

【摘要】：建筑物作為城市三維建模的重要目標(biāo),其位置邊界信息在目前地圖更新與導(dǎo)航、房產(chǎn)規(guī)劃等應(yīng)用方面起著重要的作用。地面三維激光掃描儀能夠獲取大面積高分辨率的被測對象表面海量三維數(shù)據(jù),但原始三維激光掃描數(shù)據(jù)中除了建筑物立面目標(biāo)點云外,還包含一些地面、樹木、行人、車輛和城市的部分基礎(chǔ)設(shè)施點云等非建筑物點云數(shù)據(jù)及其他噪聲點,需要進一步提取建筑物立面信息,以便對目標(biāo)建筑進行三維建模。因此,如何高效的從大量點云數(shù)據(jù)中快速準(zhǔn)確的提取出建筑物立面,成為了許多學(xué)者研究的熱點方向,是“數(shù)字城市”建設(shè)工作中的重要環(huán)節(jié),提取準(zhǔn)確度決定著建筑物后期模型的重建精度。隨機抽樣一致性算法(Random Sample Consensus)作為一種魯棒性強的算法,在數(shù)據(jù)錯誤率超過50%時仍然能夠得到理想的處理結(jié)果,能有效抑制噪聲點的影響,提取出正確的特征線和特征面,是一種穩(wěn)健、高效的從樣本集中擬合數(shù)學(xué)要素的方法,在基礎(chǔ)矩陣估計、特征匹配、運動模型選擇等計算機視覺領(lǐng)域內(nèi)有著廣泛應(yīng)用。目前RANSAC算法多用于機載和車載LiDAR數(shù)據(jù)處理上,在地面三維激光掃描儀獲取數(shù)據(jù)中的研究較少。傳統(tǒng)的RANSAC算法需要事先確定閾值,在進行建筑物立面信息的平面點云提取過程中,閾值的選取對平面提取準(zhǔn)確性有很大影響。論文在簡要介紹地面三維激光掃描技術(shù)的基礎(chǔ)上,闡述了RANSAC算法的原理,針對傳統(tǒng)RANSAC算法的特點和不足,對傳統(tǒng)的基于RANSAC方法提取建筑物立面進行了改進。采用基于點云密度值和半徑密度優(yōu)化了RANSAC算法,通過實驗完成了一建筑物立面提取。實驗結(jié)果表明改進后的方法在建筑物立面提取的準(zhǔn)確度上有明顯的提高。研究了曲面重構(gòu)法和參數(shù)法兩種不同的建模方法,分別對獲取的兩個點云數(shù)據(jù)進行了模型重建,闡述了兩種模型重建方法具體的操作步驟,并將兩種建模技術(shù)進行了結(jié)合使用,最后對兩種建模技術(shù)進行對比分析。
[Abstract]:As an important goal of urban 3D modeling, the location and boundary information of buildings plays an important role in map updating and navigation, real estate planning and other applications. The measured data of 3D object surface terrestrial 3D laser scanner to obtain a large area of high resolution, but the original 3D laser scanning data in addition to building facade object point cloud, also contains some ground, trees, pedestrians, vehicles and parts of the city infrastructure and other non point cloud building point cloud and other noise, need further extraction of building facade information for 3D modeling of buildings. Therefore, how to extract building facade quickly and accurately from a large number of cloud data has become a hot research direction of many scholars. It is an important link in the construction of "digital city". The accuracy of extraction determines the accuracy of reconstruction. Random consistency algorithm (Random Sample Consensus) as a robust algorithm, still can get ideal results rate of more than 50% in the data error, can effectively suppress the influence of noise, feature extraction and feature line is correct, is a robust and efficient method on mathematical fitting elements from the sample, based on matrix estimation, feature matching and motion model selection in the field of computer vision has been widely used. At present, the RANSAC algorithm is mostly used in airborne and vehicle LiDAR data processing, and there is less research in the acquisition of data in the ground three-dimensional laser scanner. The traditional RANSAC algorithm needs to determine the threshold beforehand. During the process of building elevation information extraction, the threshold selection has great influence on the accuracy of plane extraction. Based on the brief introduction of the terrestrial 3D laser scanning technology, this paper expounds the principle of RANSAC algorithm, and improves the traditional RANSAC algorithm based on the RANSAC method. The RANSAC algorithm is optimized based on the point cloud density value and the radius density, and a building facade is extracted by experiment. The experimental results show that the improved method has an obvious improvement in the accuracy of the building elevation. The study of two different modeling methods of surface reconstruction method and parameter method, respectively on the two point cloud data acquisition of model reconstruction, expounds the operation steps of two kinds of model reconstruction method, and two kinds of modeling techniques were used in combination, at the end of the two kinds of modeling techniques were compared and analyzed.
【學(xué)位授予單位】：東華理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P225.2;TU198

【參考文獻】

相關(guān)期刊論文 前10條

1 林卉;王李娟;康志忠;田艷鳳;邵聰穎;;三維激光掃描建筑物立面數(shù)據(jù)的自動提取[J];測繪通報;2016年10期

2 彭晨;余柏蒗;吳賓;吳健平;;基于移動激光掃描點云特征圖像和SVM的建筑物立面半自動提取方法[J];地球信息科學(xué)學(xué)報;2016年07期

3 陳源愷;;地面固定式激光掃描點云中建筑物墻面信息的識別提取[J];測繪與空間地理信息;2015年11期

4 羅寒;王建強;;基于地面三維激光掃描兩種模型重建技術(shù)[J];測繪與空間地理信息;2015年07期

5 王瑞巖;姜光;丁瑜禎;高全學(xué);;大規(guī)模城市三維重建中點云配準(zhǔn)及平面提取研究[J];信號處理;2015年06期

6 化蕾;黃洪宇;陳崇成;黃淑華;;基于激光點云數(shù)據(jù)的客家土樓三維建模[J];遙感技術(shù)與應(yīng)用;2015年01期

7 苗啟廣;郭雪;宋建鋒;宣賀君;;基于區(qū)域預(yù)測的LiDAR點云數(shù)據(jù)形態(tài)學(xué)濾波算法[J];激光與光電子學(xué)進展;2015年01期

8 龐世燕;劉亞文;左志奇;陳正富;;結(jié)合區(qū)域增長法和TIN邊緣分割的建筑物立面幾何特征提取[J];武漢大學(xué)學(xué)報(信息科學(xué)版);2015年01期

9 李永強;呂海洋;蘇蕾;王文越;曹鴻;楊莎莎;;車載LiDAR點云中建筑物立面的自動分割[J];測繪科學(xué);2014年04期

10 陳璋雯;達飛鵬;;基于模糊熵迭代的三維點云精簡算法[J];光學(xué)學(xué)報;2013年08期

相關(guān)會議論文 前1條

1 毛鋒;周文生;李強;黃健熙;;空間信息技術(shù)在京杭大運河文化遺產(chǎn)保護中的應(yīng)用[A];《測繪通報》測繪科學(xué)前沿技術(shù)論壇摘要集[C];2008年

相關(guān)碩士學(xué)位論文 前2條

1 倪歡;面向?qū)ο蟮能囕d激光點云建筑物立面識別與三維重建[D];遼寧工程技術(shù)大學(xué);2014年

2 田佳;基于多源數(shù)據(jù)融合的三維重建研究[D];東華理工大學(xué);2012年

，

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