本文選題：點云數(shù)據(jù) + 粗配準　； 參考：《昆明理工大學(xué)》2017年碩士論文

【摘要】：三維激光測量方法憑借數(shù)據(jù)采集速度快、精度高的優(yōu)點,在三維物體和三維場景的建模中得到了廣泛的應(yīng)用。然而由于坐標測量裝置的視域范圍限制和被測物體及場景本身的尺度以及周圍環(huán)境的限制,一次掃描往往不能獲取物體及場景的全部點云數(shù)據(jù),因此需要進行不同視角下點云數(shù)據(jù)的配準拼接,以形成一個完整的場景。三維激光點云數(shù)據(jù)配準時,首先要推導(dǎo)出不同視角下點云數(shù)據(jù)之間的旋轉(zhuǎn)平移變換關(guān)系,并將獲取得到的不同視角下的點云數(shù)據(jù)統(tǒng)一到同一個坐標系下,這個過程就是實現(xiàn)對點云數(shù)據(jù)進行粗配準和精確配準。本研究針對傳統(tǒng)的點云數(shù)據(jù)配準過程中存在著精度和計算效率不高的問題展開實驗研究,試圖改進現(xiàn)有的點云配準方法。論文對點云數(shù)據(jù)配準所涉及到的基本數(shù)學(xué)原理進行了闡述。將兩個不同視角下的待配準點云分別作為源點集與目標點集,采用了基于快速點特征直方圖(FPFH)描述子以及采樣一致性方法實現(xiàn)點云數(shù)據(jù)的粗配準。先對點云數(shù)據(jù)提取關(guān)鍵點,計算關(guān)鍵點的表面法線,進一步利用法線特征計算快速點特征直方圖(FPFH)描述子,然后利用采樣一致性算法完成兩片點云數(shù)據(jù)的粗配準。實驗結(jié)果表明利用這一方法能夠有效的優(yōu)化點云數(shù)據(jù)的初始匹配位置。精確配準時利用粗配準得到的初始值,結(jié)合最近點迭代(ICP)算法來實現(xiàn)點云數(shù)據(jù)的精確配準。為減少點云數(shù)據(jù)的數(shù)據(jù)量以提高計算效率,引入了體素化網(wǎng)格法對點云數(shù)據(jù)進行了精簡處理,再利用RANSAC算法進行錯誤匹配點對的去除來提高配準的精度,采用上述方法構(gòu)造出精確匹配點對,利用先前研究計算出的優(yōu)化初始迭代值,進行迭代,直到滿足某個約束條件,最后完成兩片點云之間的精確配準。與傳統(tǒng)的最近點迭代算法相比,利用上述方法改進后的最近點迭代(ICP)算法在匹配準確度和計算速度上都有很大的提高。最后,結(jié)合PCL點云庫,利用斯坦福大學(xué)點云數(shù)據(jù)庫提供的bunny數(shù)據(jù)和dragonStand數(shù)據(jù)進行了實驗比對,結(jié)果表明與直接利用最近點迭代(ICP)算法相比,本文提供的方法在增大匹配度和減少計算時間方面都優(yōu)于傳統(tǒng)方法。
[Abstract]:3D laser measurement method has been widely used in 3D object and 3D scene modeling because of its advantages of fast data acquisition and high precision. However, due to the limitation of the scope of view of the coordinate measuring device, the scale of the object under measurement and the scale of the scene and the surrounding environment, the whole point cloud data of the object and scene can not be obtained by a single scan. In order to form a complete scene, the registration of point cloud data from different perspectives is needed. When 3D laser point cloud data match punctuality, we must first derive the rotation and translation transformation relationship between point cloud data from different angles of view, and unify the obtained point cloud data under the same coordinate system. This process is to achieve rough registration and accurate registration of point cloud data. In order to improve the existing point cloud registration methods, this study aims at the problems of low accuracy and low computational efficiency in the traditional point cloud data registration process. The basic mathematical principle of point cloud data registration is expounded in this paper. Based on the fast point feature histogram (FPFH) descriptor and the sampling consistency method, the rough registration of point cloud data is realized by using two different point of view cloud as the source point set and the target point set, respectively, and the fast point feature histogram (FPFH) descriptor and the sampling consistency method are used to realize the rough registration of the point cloud data. Firstly, the key points are extracted from the point cloud data, the surface normals of the key points are calculated, and the fast point feature histogram (FPFH) descriptor is further calculated by normal features, and then the rough registration of the two pieces of point cloud data is completed by using the sampling consistency algorithm. Experimental results show that this method can effectively optimize the initial matching position of point cloud data. Using the initial value obtained by rough registration and the nearest point iteration (ICP) algorithm, the accurate registration of point cloud data is realized. In order to reduce the amount of point cloud data and improve the computational efficiency, a voxel mesh method is introduced to simplify the point cloud data, and then the RANSAC algorithm is used to remove the mismatched point pairs to improve the registration accuracy. The exact matching point pairs are constructed by using the above method, and the initial iteration values calculated by the previous studies are used to iterate until a certain constraint condition is satisfied, and the exact registration between the two point clouds is finally completed. Compared with the traditional nearest point iterative algorithm, the improved nearest point iteration (ICP) algorithm improves the matching accuracy and computing speed greatly. Finally, the PCL point cloud database is used to compare the bunny data and dragonStand data provided by the point cloud database of Stanford University. The results show that the proposed algorithm is compared with the nearest point iterative algorithm. The method presented in this paper is superior to the traditional method in increasing the matching degree and reducing the computational time.
【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：1927105