本文選題：三維點云數(shù)據(jù) + 偽特征點��； 參考：《西北農(nóng)林科技大學》2017年碩士論文

【摘要】：為了快速從樹點云中得到完整的三維形態(tài)點云,點云配準必不可少。目前專家學者提出許多點云配準算法,但是樹表面粗糙,枝干纖細且相互遮擋,三維掃描儀獲得的點云不完整且存在噪點,現(xiàn)存的算法并不能完全適應(yīng)樹點云的獨特特點�；诖�,本文提出一種基于偽特征點的樹點云配準算法,該算法分為初始配準和精配準。論文的主要創(chuàng)新點及其研究內(nèi)容如下:(1)提出一種偽特征點提取算法。針對樹結(jié)構(gòu)復雜,特征點提取困難的問題,采用偽特征點提取算法提取樹點云的偽特征點,該方法通過一次分簇、二次分簇、計算偽特征點等步驟完成偽特征點提取,達到使用較少的點精細顯示點云特征的目的,得到較好的偽特征點集。實驗結(jié)果表明,與基于幾何特征的特征點提取算法相比,偽特征點提取算法更適應(yīng)于樹點云的特征。(2)提出一種基于偽特征點的樹點云配準算法。針對獲取點云數(shù)據(jù)稠密,配準較為耗時的問題,在初始配準中使用提取的偽特征點粗略的調(diào)整兩片點云的位置,減少精配準的迭代次數(shù),提高配準效率。針對樹點云中的噪聲點影響提取對應(yīng)點對正確率的問題,本文在初始配準及其精配準中采用鄰域信息分布的相似性來篩除錯誤的對應(yīng)點對,提高對應(yīng)點對的正確率。并針對初始配準和精配準所使用數(shù)據(jù)的不同特點,分別使用夾角和距離來度量鄰域分布的相似性,提高對應(yīng)點對的正確率,從而改善配準精度。(3)針對基于偽特征點的樹點云配準算法的配準性能驗證問題,使用有葉及無葉樹點云驗證該算法的有效性;使用非樹點云驗證該算法的可擴展性;并在相同實驗環(huán)境與實驗數(shù)據(jù)的前提下,與其它配準算法比較的方法,驗證該算法的優(yōu)越性。實驗表明,在相同迭代次數(shù)的前提下,該算法的配準誤差比ICP(Iterative Closed Point)算法的配準誤差減少41.1%,比SICP(Sparse ICP)算法的配準誤差減少16.8%。另外,論文還使用盆栽模型、Bunny等模型來驗證算法的通用性。實驗表明,該算法也能夠配準非樹點云,具有較強通用性。
[Abstract]:In order to get a complete three-dimensional morphological point cloud from the tree point cloud, registration of point clouds is essential. At present, experts and scholars have proposed many point cloud registration algorithms, but the tree surface is rough, the branches are thin and each other is obscured. The point cloud obtained by the 3D scanner is incomplete and has noise. The existing algorithms do not fully adapt to the unique special characteristics of the tree point cloud. Based on this, this paper proposes a tree point cloud registration algorithm based on pseudo feature points, which is divided into initial registration and fine registration. The main innovation points and their research contents are as follows: (1) a pseudo feature point extraction algorithm is proposed. The pseudo feature point extraction algorithm is used to extract tree points for the problem of complex tree structure and the difficulty of extracting feature points. The pseudo feature point of a cloud is extracted from a cluster, two clusters and a pseudo feature point to extract the pseudo feature points. A better pseudo feature point set is obtained. The experimental results show that the pseudo feature extraction algorithm is better than the geometric feature extraction algorithm based on the feature point extraction algorithm. To adapt to the feature of tree point cloud. (2) a registration algorithm for tree point cloud based on pseudo feature points is proposed. In order to obtain the dense data of the point cloud, the registration is more time-consuming. In the initial registration, the extracted pseudo feature points are used to roughly adjust the position of two point cloud, reducing the number of iterations of the precise registration and improving the registration efficiency. The noise point affects the correct rate of the extraction of the corresponding point. In this paper, we use the similarity of the neighborhood information distribution to screen out the corresponding point pairs in the initial registration and the fine registration, and improve the correct rate of the corresponding point pairs. The similarity of the cloth improves the correct rate of the corresponding point pair and improves the registration accuracy. (3) the validity of the algorithm is verified by using the leaf and leaf free tree point cloud to verify the validity of the registration performance verification problem based on the pseudo feature point based tree point cloud registration algorithm, and the scalability of the algorithm is verified by using non tree point cloud, and the experimental data are also used in the same experimental environment and experimental data. Under the premise of comparison with other registration algorithms, the superiority of the algorithm is verified. The experiment shows that the registration error of the algorithm is less than that of the ICP (Iterative Closed Point) algorithm by 41.1%, and the registration error of the SICP (Sparse ICP) algorithm is less 16.8%. than the SICP (Sparse ICP) algorithm. The paper also uses the pot model, Bunny and other models are used to verify the universality of the algorithm. Experiments show that the algorithm can also register non tree point clouds, and has strong versatility.

【學位授予單位】：西北農(nóng)林科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：S126

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