本文選題：點云配準(zhǔn) + ICP��； 參考：《中北大學(xué)》2017年碩士論文

【摘要】：三維點云配準(zhǔn)技術(shù)是三維重建過程中的一個重要組成部分,在各個領(lǐng)域都有十分廣泛的應(yīng)用前景。比如在工業(yè)領(lǐng)域中,可以用它來檢測物體零部件是否存在缺陷;在醫(yī)療行業(yè)中,可以用它來模擬人體器官并找出病人的病灶所在等。近些年,隨著三維掃描設(shè)備的精度不斷提高,要想得到物體精確的三維模型已經(jīng)變得非常容易。因此,三維點云數(shù)據(jù)配準(zhǔn)算法的研究也逐漸成為人們研究的重點。點云數(shù)據(jù)配準(zhǔn)的過程就是把分次測量得到的不同角度、不同參考坐標(biāo)系下的兩個或多個點云數(shù)據(jù)通過一定的旋轉(zhuǎn)和平移變換,將它們統(tǒng)一到相同的坐標(biāo)系下,從而獲得物體的完整信息并對物體進行一系列的可視化操作。目前已有的點云配準(zhǔn)算法主要存在兩方面的問題:一方面,傳統(tǒng)ICP(Iterative Closest Points,迭代最近點)算法雖然在一定程度上能夠滿足人們對實驗的要求,但它在選取對應(yīng)點時,簡單的將兩個待匹配點云中歐氏距離最近的點作為對應(yīng)點,這樣會造成一定的錯配點產(chǎn)生,從而影響算法配準(zhǔn)的精度;另一方面,當(dāng)點云數(shù)據(jù)的規(guī)模較大時,配準(zhǔn)過程中會消耗大量的時間,造成配準(zhǔn)算法實時性較差的問題。針對這些問題,本文主要從以下幾點進行研究:(1)本文深入了解了傳統(tǒng)ICP算法及其相關(guān)改進算法的配準(zhǔn)過程及存在的一些問題,并在此基礎(chǔ)上提出了基于旋轉(zhuǎn)圖像特征描述子改進的ICP算法。該算法在配準(zhǔn)前首先對待匹配點云進行了濾波處理,在減少點云數(shù)據(jù)量的同時還保持點云的基本形狀特征。然后找出兩個點云的關(guān)鍵點,分別求出待匹配點云關(guān)鍵點的旋轉(zhuǎn)圖像特征描述子,并根據(jù)兩個特征描述子的特征相似程度來確定最近點進而完成ICP配準(zhǔn),得到了較好的收斂效果。(2)為了有效解決點云規(guī)模較大時,配準(zhǔn)實時性較差的問題,本文深入了解了基于GPU(Graphics Processing Unit,圖形處理單元)的點云并行配準(zhǔn)算法。詳細介紹了EM-ICP算法和Softassign算法的配準(zhǔn)過程,并結(jié)合GPU,實現(xiàn)了基于GPU的EM-ICP和Softassign并行配準(zhǔn)算法,大幅度提高了點云的配準(zhǔn)的效率,提高了算法的實時性。(3)在本文提出的改進算法的基礎(chǔ)上設(shè)計并實現(xiàn)了基于改進ICP算法的點云配準(zhǔn)系統(tǒng),并通過編程的方式詳細設(shè)計和分析了該系統(tǒng)中的每個模塊。該系統(tǒng)主要分為點云顯示、點云濾波模塊與點云配準(zhǔn)模塊,其中點云配準(zhǔn)模塊使用了本文提出的改進ICP算法。
[Abstract]:3D point cloud registration technology is an important part of 3D reconstruction process and has a very wide application prospect in various fields. For example, in the industrial field, it can be used to detect the defects of object parts, and in the medical industry, it can be used to simulate human organs and find out where the patient's lesions are. In recent years, with the improvement of the accuracy of 3D scanning equipment, it has become very easy to obtain the accurate 3D model of object. Therefore, the research of three-dimensional point cloud data registration algorithm has gradually become the focus of research. The registration process of point cloud data is to unify two or more point cloud data in different reference coordinate systems into the same coordinate system by a certain rotation and translation transformation. Thus the complete information of the object is obtained and a series of visualization operations are carried out. There are two main problems in the existing point cloud registration algorithms: on the one hand, the traditional ICP(Iterative Closest points (iterative nearest points) algorithm can meet the requirements of experiments to some extent, but it selects the corresponding points. Simply taking the nearest Euclidean point in the cloud of two points to be matched as the corresponding point, this will result in a certain mismatch point, which will affect the accuracy of the algorithm registration; on the other hand, when the data of point cloud is large, Registration process will consume a lot of time, resulting in poor real-time registration algorithm. Aiming at these problems, this paper mainly studies the following points: (1) this paper deeply understand the registration process and some problems of the traditional ICP algorithm and its related improved algorithm. On this basis, an improved ICP algorithm based on rotating image feature descriptor is proposed. In this algorithm, the matching point cloud is filtered before registration, and the basic shape feature of the point cloud is kept while the data of point cloud is reduced. Then the key points of the two point clouds are found, and the rotating image feature descriptors of the key points to be matched are obtained, and the nearest points are determined according to the similarity degree of the two feature descriptors and the ICP registration is completed. In order to effectively solve the problem of poor real-time registration when the point cloud scale is large, this paper deeply understand the point cloud parallel registration algorithm based on GPU(Graphics Processing unit (graphic processing unit). The registration process of EM-ICP algorithm and Softassign algorithm is introduced in detail, and the parallel registration algorithm of EM-ICP and Softassign based on GPU is realized, which greatly improves the efficiency of point cloud registration. Based on the improved algorithm proposed in this paper, a point cloud registration system based on improved ICP algorithm is designed and implemented. Each module of the system is designed and analyzed in detail by programming. The system is mainly divided into point cloud display, point cloud filtering module and point cloud registration module, where the point cloud registration module uses the improved ICP algorithm proposed in this paper.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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本文編號：1956616