本文選題：車載移動測量系統(tǒng) + 數(shù)據(jù)配準��； 參考：《武漢大學》2014年博士論文

【摘要】：車載移動測量系統(tǒng)集成了激光掃描儀、數(shù)碼相機、GPS、里程計、慣性測量單元等多種傳感器,不僅能快速獲取包含物體空間坐標信息的激光掃描數(shù)據(jù),還能獲取包含豐富紋理信息的光學影像,具有數(shù)據(jù)獲取速度快、場景目標豐富的特點,已成為一種新型、集成、高效的空間信息獲取的技術(shù)手段,廣泛應(yīng)用于地理國情監(jiān)測和智慧城市的各項建設(shè)。由于光學影像和激光掃描數(shù)據(jù)對目標物的描述存在諸多互補性,為了提高城市場景三維可視化效果,需要發(fā)展高效的光學影像與激光點云的配準方法。另外,車載移動測量系統(tǒng)獲取的點云數(shù)據(jù)具有海量特性,點云數(shù)據(jù)處理耗時長、計算量大,再加上場景復(fù)雜,不同目標分類的自動化和智能化程度低,這些問題限制了車載移動測量系統(tǒng)在移動測圖、基礎(chǔ)測繪等應(yīng)用的實際功效。 本文針對上述存在的問題,重點研究和探討車載激光點云和全景影像的精確配準方法,以及激光點云的高效分類識別技術(shù),主要進行了以下幾方面的工作： 1.總結(jié)了當前國內(nèi)外車載移動測量系統(tǒng)在光學影像與激光點云數(shù)據(jù)配準、車載激光點云分類方面的研究進展。對現(xiàn)有的相關(guān)技術(shù)和研究方法進行歸納并分析其優(yōu)缺點,針對現(xiàn)有數(shù)據(jù)配準及分類中的不足和難點,確定了本文的研究目標與內(nèi)容。 2.闡述了車載移動測量中的相關(guān)基本原理。包括系統(tǒng)的組成及工作原理,分析了各傳感器間數(shù)據(jù)的流轉(zhuǎn)關(guān)系；介紹了全景影像的成像原理,對魚眼鏡頭成像的光學基礎(chǔ)、成像過程、魚眼圖像變形特征及糾正算法進行分析；闡述了車載激光掃描儀測距的原理、方式及其對三維空間的表征方式。 3.提出了一種基于投影回歸的全景影像采集和激光掃描配準方法。根據(jù)光路傳播的可逆原理,把魚眼鏡頭成像理解為一個以投影中心為光源點的光線發(fā)射過程,提出通過把魚眼鏡頭的光源和激光掃描儀的光源進行歸一重合,實現(xiàn)魚眼圖像與激光點云配準的思想,對投影回歸方法的正確性進行了理論證明。結(jié)合具體的實驗數(shù)據(jù),對魚眼圖像糾正、配準、拼接、分割并最終紋理映射成球形全景,對激光點云數(shù)據(jù)進行了配準、除噪和簡化處理,并把處理好的點云投影到與球形全景圖共球心的虛擬投影球面上,實現(xiàn)了全景影像與激光點云的配準。該配準方法不受區(qū)域灰度及幾何特征信息的制約。 4.配準量測精度的驗證研究。在全景影像與激光點云配準的基礎(chǔ)上,提出了一種采用角度逼近法來獲取球面投影中距離待測點最近鄰的激光點的方法,把對影像上的量測轉(zhuǎn)換為對最近鄰激光掃描點坐標的查詢與計算,對角度逼近法的正確性進行了數(shù)學證明,并針對不同角度分辨率下激光點云與全景影像的配準精度進行了驗證與分析,結(jié)果表明采用投影回歸原理的配準方法具有較好的精確度。 5.基于知識與特征圖像的點云分類研究。采用橫軸圓柱投影和正射投影分別生成了點云的空間特征圖像、回波強度圖像和顏色特征圖像,基于知識和特征圖像對行道樹點云提出了分層投影、疊加分析的分類方法,即通過高程閾值對樹干點云和樹冠點云分別投影,得到相應(yīng)的特征圖像,再根據(jù)灰度值把特征圖像轉(zhuǎn)換成二值圖像,并對二值圖像進行疊加與分析,最后通過知識濾波來提取行道樹點云數(shù)據(jù)。論文結(jié)合具體數(shù)據(jù)進行了分類實驗,實驗結(jié)果表明,該方法對一些混雜在行道樹的噪聲信息處理的比較好,且該方法把三維點云轉(zhuǎn)化為二值圖像進行處理,有效避免了大量的幾何運算,顯著降低了分類算法的復(fù)雜度。說明采用分層投影、疊加分析的方法對行道樹點云提取是行之有效的。 6.基于機器學習的點云分類研究。以激光點云對象的原始特征為基礎(chǔ),通過對其周邊點群的上下文語義環(huán)境進行分析,充分利用點云的空間分布特征及其局部幾何特征,歸納計算了點云對象的新特征,最終構(gòu)建了由17個特征組成的點云特征向量。采用支持向量機和人工神經(jīng)網(wǎng)絡(luò)模型對行道樹分類識別進行實驗,在支持向量機分類過程中,為了提高模型的泛化能力,分別采用粒子群優(yōu)化算法和遺傳算法對模型參數(shù)進行尋優(yōu),分析了不同算法的學習曲線特征,最終采用粒子群優(yōu)化算法,針對不同訓(xùn)練樣本、不同特征向量進行了點云分類識別的系列實驗。論文選取支持向量機最優(yōu)分類結(jié)果所對應(yīng)的實驗條件,采用人工神經(jīng)網(wǎng)絡(luò)的方法對點云分類進行實驗對比驗證,兩種方法均取得整體較為滿意的實驗結(jié)果,表明了機器學習方法在車載激光點云自動分類中的適用性,為提高車載激光點云分類自動化程度和智能化水平提供了新的思路。
[Abstract]:The vehicle mobile measurement system integrates a variety of sensors, such as laser scanner, digital camera, GPS, odometer, inertial measurement unit and so on. It can not only quickly obtain the laser scanning data containing the space coordinate information of the object, but also obtain the optical image containing rich texture information, which has the characteristics of fast data acquisition and rich scene target. As a new, integrated and efficient technology for obtaining spatial information, it is widely used in geographical conditions monitoring and the construction of intelligent cities. The description of objects in the optical image and laser scanning data has many complementarities. In order to improve the effect of the three-dimensional visualization of the city scene, it is necessary to develop high efficient optical images. In addition, the point cloud data obtained by the vehicle mobile measurement system have massive characteristics, the point cloud data processing is time-consuming, the amount of computation is large, the scene is complex, and the automation and intelligence of different target classification are low. These problems restrict the application of mobile measurement system in mobile mapping and basic surveying and mapping. Practical effect.
Aiming at the above problems, this paper focuses on the research and Discussion on the accurate registration method of the vehicle laser point cloud and panoramic image, as well as the efficient classification and recognition technology of the laser point cloud.
1. summarize the research progress of the vehicle mobile measurement system at home and abroad on the registration of optical image and laser point cloud data and the classification of the on-board laser point cloud. The existing related technologies and research methods are summed up and analyzed their advantages and disadvantages. In view of the shortcomings and difficulties of the existing data registration and classification, the research objectives of this paper are determined. And content.
2. the basic principles of the vehicle mobile measurement are described, including the composition and working principle of the system, the flow relationship between the various sensors is analyzed, the imaging principle of the panoramic image is introduced, the optical foundation of the fish eye lens imaging, the imaging process, the distortion characteristic and the correction algorithm of the fish eye image are analyzed, and the vehicular excitation is expounded. The principle and method of optical scanner ranging and its representation of three-dimensional space are discussed.
3. a method of panoramic image acquisition and laser scanning registration based on projection regression is proposed. According to the reversible principle of optical path propagation, the fish eye lens imaging is understood as a light emitting process with the center of projection as the light source. By combining the light source of the fish eye lens and the light source of the laser scavenger, the fish eye is realized. The idea of registration of image and laser point cloud is used to prove the correctness of the projection regression method. Combined with the specific experimental data, the fish eye image is corrected, registered, spliced, segmented and mapped into a spherical panoramic view, and the laser point cloud data are registered, denoising and simplifying, and projecting the treated point clouds to the ball. The registration of the panoramic image with the laser point cloud is realized on the virtual projection sphere of the central panorama. The registration method is not restricted by the information of the gray and geometric features of the region.
4., on the basis of the registration of the panoramic image and the laser point cloud, a method of using angle approximation to obtain the laser point from the nearest neighbor in the spherical projection is proposed, and the measurement of the image is converted to the query and calculation of the coordinates of the nearest neighbor laser scanning point, and the angle approximation method is used. The correctness is proved by mathematics, and the registration accuracy of the laser point cloud and the panoramic image at different angle resolution is verified and analyzed. The results show that the registration method using the projection regression principle has good accuracy.
5. the study of point cloud classification based on knowledge and feature images. Using horizontal cylindrical projection and orthophoto projection, the spatial feature images of point clouds, echo intensity images and color feature images are generated respectively. Based on knowledge and feature images, the hierarchical projection of the road tree point cloud is put forward, and the superposition classification method is used, that is, to the tree trunk through the elevation threshold. The point cloud and the crown point cloud are projected separately, and the corresponding feature images are obtained. Then the feature images are converted into two value images according to the gray value, and the two value images are superimposed and analyzed. Finally, the data of the street tree point clouds are extracted by the knowledge filtering. The paper is classified by the concrete data. The experimental results show that the method is mixed with some of the data. The noise information processing of the miscellaneous tree is better, and this method transforms the three dimensional point cloud into two value images, which effectively avoids a large number of geometric operations and significantly reduces the complexity of the classification algorithm. It is proved that the method of stratified projection and superposition analysis is effective for the extraction of the road tree point cloud.
6. based on the point cloud classification based on machine learning. Based on the original features of the laser point cloud objects, the context semantic environment of the surrounding point groups is analyzed. The spatial distribution features and local geometric features of the point clouds are fully utilized and the new characteristics of the point cloud objects are summed and calculated. Finally, a point cloud consisting of 17 features is constructed. In order to improve the generalization ability of the model, in order to improve the generalization ability of the model, the particle swarm optimization algorithm and genetic algorithm are used to optimize the model parameters, and the characteristics of the learning curve of different algorithms are analyzed. Finally, the characteristics of the learning curve of different algorithms are analyzed. The particle swarm optimization algorithm is used to carry out a series of experiments on the classification of point clouds for different training samples and different eigenvectors. The paper selects the experimental conditions corresponding to the optimal classification results of support vector machines, and uses artificial neural network to test the point cloud classification by experiment. The two methods have obtained the whole more satisfactory experiment. The result shows the applicability of machine learning method in automatic classification of vehicle laser point cloud, which provides a new idea for improving the automation and intelligence level of vehicle laser point cloud classification.
【學位授予單位】：武漢大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：P208;P234;TP391.41

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