本文關(guān)鍵詞： 雙目視覺(jué) 立體匹配 Hopfield神經(jīng)網(wǎng)絡(luò) 目標(biāo)定位 三維重建　出處：《廣東工業(yè)大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：機(jī)器視覺(jué)是機(jī)器人感知外界環(huán)境的一種重要手段,而深度信息是機(jī)器人利用機(jī)器視覺(jué)感知外界的一個(gè)重要信息。雙目視覺(jué)是機(jī)器視覺(jué)領(lǐng)域的一個(gè)分支,也是一種獲取深度信息的重要手段。雙目視覺(jué)通過(guò)在不同角度觀察同一目標(biāo),對(duì)產(chǎn)生的圖像進(jìn)行特征提取、立體匹配和視差計(jì)算,并進(jìn)行重建三維場(chǎng)景來(lái)獲取深度信息。雙目視覺(jué)涉及的主要內(nèi)容有:圖像獲取、相機(jī)標(biāo)定、特征提取、立體匹配和三維重建。其中,立體匹配是雙目視覺(jué)研究中的重點(diǎn)和難點(diǎn)。本文提出基于Hopfield神經(jīng)網(wǎng)絡(luò)的立體匹配算法,利用雙目視覺(jué),研究了機(jī)器人目標(biāo)定位技術(shù),并在此基礎(chǔ)上進(jìn)行了基于雙目視覺(jué)的機(jī)器人目標(biāo)定位實(shí)驗(yàn)。首先,本文對(duì)國(guó)內(nèi)外基于雙目視覺(jué)的機(jī)器人目標(biāo)定位技術(shù)進(jìn)行了深入研究,了解研究現(xiàn)狀,總結(jié)分析了基于雙目視覺(jué)的機(jī)器人目標(biāo)定位技術(shù)中涉及的研究重點(diǎn)和難點(diǎn);明確了本文的主要研究?jī)?nèi)容和研究工作。其次,研究了立體視覺(jué)基本原理,特別是雙目視覺(jué)理論。在分析比較雙目視覺(jué)系統(tǒng)原理的基礎(chǔ)上,本文采用基于平行光軸的雙目視覺(jué)系統(tǒng)作為機(jī)器人目標(biāo)的定位的基本結(jié)構(gòu),并從幾何角度分析了目標(biāo)深度與視差之間的關(guān)系,奠定了雙目視覺(jué)目標(biāo)定位的理論基礎(chǔ)。再次,重點(diǎn)研究了雙目視覺(jué)目標(biāo)定位涉及的關(guān)鍵技術(shù),特別是坐標(biāo)系統(tǒng)、相機(jī)模型、相機(jī)標(biāo)定、雙目相機(jī)標(biāo)定與校正、三維重建等技術(shù);另外,重點(diǎn)研究了基于張正友法的相機(jī)標(biāo)定技術(shù),并采用該方法對(duì)本文實(shí)驗(yàn)使用的相機(jī)進(jìn)行標(biāo)定。然后,深入研究了雙目立體匹配,在分析研究了雙目立體匹配基本原理、立體匹配約束、立體匹配難點(diǎn)和立體匹配算法的基礎(chǔ)上,利用能量最小化方法,提出一種基于Hopfield神經(jīng)網(wǎng)絡(luò)的立體匹配算法;立體匹配中的極線約束、唯一性約束、平滑性約束和相似性約束引入到Hopfield神經(jīng)網(wǎng)絡(luò)能量函數(shù)中,不斷更新神經(jīng)元狀態(tài),從而最小化Hopfield神經(jīng)網(wǎng)絡(luò)能量函數(shù),最終計(jì)算出視差圖。最后,在理論研究的基礎(chǔ)上,搭建了基于雙目視覺(jué)的機(jī)器人目標(biāo)定位實(shí)驗(yàn)平臺(tái);在平臺(tái)上分別開(kāi)展了相機(jī)標(biāo)定、雙目相機(jī)標(biāo)定、基于Hopfield神經(jīng)網(wǎng)絡(luò)立體匹配和機(jī)器人目標(biāo)定位等實(shí)驗(yàn),并進(jìn)行誤差分析。在不斷實(shí)驗(yàn)并分析誤差,改進(jìn)算法的基礎(chǔ)上,完成了基于雙目視覺(jué)的機(jī)器人目標(biāo)定位,達(dá)到了令人滿意的精度。
[Abstract]:Machine vision is an important means for robot to perceive the external environment, and depth information is an important information for robot to use machine vision to perceive external environment. Binocular vision is a branch of machine vision field. Binocular vision is also an important means of obtaining depth information. Binocular vision can extract features, stereo matching and parallax calculation of the generated image by observing the same object from different angles. The main contents of binocular vision are: image acquisition, camera calibration, feature extraction, stereo matching and 3D reconstruction. Stereo matching is an important and difficult point in binocular vision research. In this paper, a stereo matching algorithm based on Hopfield neural network is proposed. On this basis, the robot target localization experiment based on binocular vision is carried out. Firstly, this paper makes a deep research on the robot target location technology based on binocular vision at home and abroad, and understands the current situation of the research. This paper summarizes and analyzes the key points and difficulties involved in the robot target location technology based on binocular vision, clarifies the main research contents and research work in this paper. Secondly, the basic principle of stereo vision is studied. Especially the theory of binocular vision. On the basis of analyzing and comparing the principle of binocular vision system, this paper adopts the binocular vision system based on parallel optical axis as the basic structure of robot target localization. The relationship between target depth and parallax is analyzed from the angle of geometry, and the theoretical foundation of binocular visual target localization is established. Thirdly, the key technologies involved in binocular visual target localization, especially coordinate system and camera model, are studied emphatically. Camera calibration, binocular camera calibration and correction, 3D reconstruction, etc. In addition, the camera calibration technology based on Zhang Zhengyou method is studied, and the camera used in this experiment is calibrated by this method. The basic principle of binocular stereo matching, the constraint of stereo matching, the difficulty of stereo matching and the algorithm of stereo matching are analyzed, and the energy minimization method is used. A stereo matching algorithm based on Hopfield neural network is proposed, in which polar line constraint, uniqueness constraint, smoothness constraint and similarity constraint are introduced into the energy function of Hopfield neural network to update the neuron state. In order to minimize the energy function of Hopfield neural network, finally calculate the parallax map. Finally, based on the theoretical research, a robot target location experimental platform based on binocular vision is built, and the camera calibration is carried out on the platform. Binocular camera calibration, stereo matching based on Hopfield neural network and robot target positioning experiments, and error analysis. On the basis of continuous experiments and error analysis, improved algorithm, the robot target location based on binocular vision is completed. Satisfactory accuracy has been achieved.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41;TP242

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本文編號(hào)：1513912