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

【摘要】：為了快速?gòu)臉?shù)點(diǎn)云中得到完整的三維形態(tài)點(diǎn)云,點(diǎn)云配準(zhǔn)必不可少。目前專(zhuān)家學(xué)者提出許多點(diǎn)云配準(zhǔn)算法,但是樹(shù)表面粗糙,枝干纖細(xì)且相互遮擋,三維掃描儀獲得的點(diǎn)云不完整且存在噪點(diǎn),現(xiàn)存的算法并不能完全適應(yīng)樹(shù)點(diǎn)云的獨(dú)特特點(diǎn)。基于此,本文提出一種基于偽特征點(diǎn)的樹(shù)點(diǎn)云配準(zhǔn)算法,該算法分為初始配準(zhǔn)和精配準(zhǔn)。論文的主要?jiǎng)?chuàng)新點(diǎn)及其研究?jī)?nèi)容如下:(1)提出一種偽特征點(diǎn)提取算法。針對(duì)樹(shù)結(jié)構(gòu)復(fù)雜,特征點(diǎn)提取困難的問(wèn)題,采用偽特征點(diǎn)提取算法提取樹(shù)點(diǎn)云的偽特征點(diǎn),該方法通過(guò)一次分簇、二次分簇、計(jì)算偽特征點(diǎn)等步驟完成偽特征點(diǎn)提取,達(dá)到使用較少的點(diǎn)精細(xì)顯示點(diǎn)云特征的目的,得到較好的偽特征點(diǎn)集。實(shí)驗(yàn)結(jié)果表明,與基于幾何特征的特征點(diǎn)提取算法相比,偽特征點(diǎn)提取算法更適應(yīng)于樹(shù)點(diǎn)云的特征。(2)提出一種基于偽特征點(diǎn)的樹(shù)點(diǎn)云配準(zhǔn)算法。針對(duì)獲取點(diǎn)云數(shù)據(jù)稠密,配準(zhǔn)較為耗時(shí)的問(wèn)題,在初始配準(zhǔn)中使用提取的偽特征點(diǎn)粗略的調(diào)整兩片點(diǎn)云的位置,減少精配準(zhǔn)的迭代次數(shù),提高配準(zhǔn)效率。針對(duì)樹(shù)點(diǎn)云中的噪聲點(diǎn)影響提取對(duì)應(yīng)點(diǎn)對(duì)正確率的問(wèn)題,本文在初始配準(zhǔn)及其精配準(zhǔn)中采用鄰域信息分布的相似性來(lái)篩除錯(cuò)誤的對(duì)應(yīng)點(diǎn)對(duì),提高對(duì)應(yīng)點(diǎn)對(duì)的正確率。并針對(duì)初始配準(zhǔn)和精配準(zhǔn)所使用數(shù)據(jù)的不同特點(diǎn),分別使用夾角和距離來(lái)度量鄰域分布的相似性,提高對(duì)應(yīng)點(diǎn)對(duì)的正確率,從而改善配準(zhǔn)精度。(3)針對(duì)基于偽特征點(diǎn)的樹(shù)點(diǎn)云配準(zhǔn)算法的配準(zhǔn)性能驗(yàn)證問(wèn)題,使用有葉及無(wú)葉樹(shù)點(diǎn)云驗(yàn)證該算法的有效性;使用非樹(shù)點(diǎn)云驗(yàn)證該算法的可擴(kuò)展性;并在相同實(shí)驗(yàn)環(huán)境與實(shí)驗(yàn)數(shù)據(jù)的前提下,與其它配準(zhǔn)算法比較的方法,驗(yàn)證該算法的優(yōu)越性。實(shí)驗(yàn)表明,在相同迭代次數(shù)的前提下,該算法的配準(zhǔn)誤差比ICP(Iterative Closed Point)算法的配準(zhǔn)誤差減少41.1%,比SICP(Sparse ICP)算法的配準(zhǔn)誤差減少16.8%。另外,論文還使用盆栽模型、Bunny等模型來(lái)驗(yàn)證算法的通用性。實(shí)驗(yàn)表明,該算法也能夠配準(zhǔn)非樹(shù)點(diǎn)云,具有較強(qiáng)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(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：S126

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