本文關(guān)鍵詞： 非合作目標(biāo) 雙目視覺(jué) 目標(biāo)識(shí)別 位姿測(cè)量 Kalman濾波　出處：《北京理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：目前,以傳統(tǒng)航天器的在軌維護(hù)、太空垃圾清理等為目的的空間非合作目標(biāo)捕獲技術(shù)成為空間機(jī)械臂領(lǐng)域新的發(fā)展方向。非合作目標(biāo)的視覺(jué)導(dǎo)航與跟蹤技術(shù)作為捕獲過(guò)程中的關(guān)鍵技術(shù)更是各國(guó)研究人員的研究熱點(diǎn)。為了解決非合作目標(biāo)的視覺(jué)導(dǎo)航和跟蹤問(wèn)題,本文主要對(duì)非合作目標(biāo)位姿測(cè)量和跟蹤技術(shù)進(jìn)行了研究。本文首先建立了基于雙目立體視覺(jué)的位姿測(cè)量模型,然后研究了運(yùn)動(dòng)目標(biāo)跟蹤方法,最后通過(guò)實(shí)驗(yàn)仿真驗(yàn)證了以上模型和方法的有效性。主要內(nèi)容如下:1)對(duì)二維圖像進(jìn)行處理,提出了基于特征信息融合的非合作目標(biāo)識(shí)別方法。該方法針對(duì)非合作目標(biāo)無(wú)法提供有效合作信息的問(wèn)題,以航天器自有的特征信息作為識(shí)別對(duì)象,得到非合作目標(biāo)矩形和圓形特征的基本形狀信息,完成了目標(biāo)識(shí)別和平面中心判定。并且根據(jù)尺度不變特征變換算法(scale invariant feature transform algorithm,sift)對(duì)左右相機(jī)獲取的圖像進(jìn)行特征點(diǎn)匹配。2)研究了基于圖像處理的雙目視覺(jué)相對(duì)位姿測(cè)量方案。該方案選取目標(biāo)衛(wèi)星本體平面的中心點(diǎn)作為目標(biāo)坐標(biāo)系原點(diǎn),建立非合作目標(biāo)坐標(biāo)系,計(jì)算相對(duì)位姿。然后建立了立體深度和視差的關(guān)系模型,分析了位姿計(jì)算的誤差來(lái)源。最后選用OpenGL進(jìn)行建模仿真,驗(yàn)證提出方案的可行性、精度與實(shí)時(shí)性。3)實(shí)現(xiàn)了非合作目標(biāo)的視覺(jué)追蹤。針對(duì)跟蹤高機(jī)動(dòng)運(yùn)動(dòng)目標(biāo)的非線性問(wèn)題,本文基于Kalman濾波算法引入IMM算法,加強(qiáng)了速度跟蹤的性能。并且分別在目標(biāo)勻速直線、勻加速直線和復(fù)雜曲線運(yùn)動(dòng)模型下比較了兩種濾波算法的預(yù)測(cè)性能。具體實(shí)驗(yàn)分析證明,本文提出的非合作目標(biāo)識(shí)別方法有效保證了目標(biāo)識(shí)別的準(zhǔn)確性,位姿測(cè)量方案和IMM濾波追蹤方法準(zhǔn)確、簡(jiǎn)便易行;算法的改進(jìn)和模型的優(yōu)化能較顯著提高對(duì)非合作目標(biāo)航天器的視覺(jué)導(dǎo)航跟蹤精度。
[Abstract]:At present, with the on-orbit maintenance of traditional spacecraft, Space non-cooperative target capture technology, such as space garbage removal, has become a new development direction in the field of space manipulator. As a key technology in the acquisition process, the vision navigation and tracking technology of non-cooperative target is studied by many countries. In order to solve the problem of visual navigation and tracking of non-cooperative targets, In this paper, the non-cooperative target pose measurement and tracking techniques are studied. Firstly, the pose measurement model based on binocular stereo vision is established, and then the moving target tracking method is studied. Finally, the effectiveness of the above models and methods is verified by experimental simulation. The main contents are as follows: 1) the two-dimensional image is processed. A non-cooperative target recognition method based on feature information fusion is proposed, which aims at the problem that non-cooperative target can not provide effective cooperative information. The basic shape information of the rectangular and circular features of the non-cooperative target is obtained. Target recognition and plane center determination are completed. Based on scale-invariant feature transform algorithm scale invariant feature transform algorithm, feature point matching. 2) Binocular vision relative position based on image processing is studied. This scheme selects the center point of the body plane of the target satellite as the origin of the target coordinate system. The non-cooperative target coordinate system is established and the relative pose is calculated. Then the relationship model between stereo depth and parallax is established, and the error source of position and pose calculation is analyzed. Finally, OpenGL is selected for modeling and simulation, which verifies the feasibility of the proposed scheme. Aiming at the nonlinear problem of tracking high maneuvering moving targets, this paper introduces IMM algorithm based on Kalman filtering algorithm to enhance the performance of velocity tracking. The prediction performance of the two filtering algorithms is compared under the motion model of uniform acceleration line and complex curve. The experimental results show that the proposed non-cooperative target recognition method can effectively ensure the accuracy of target recognition. The position and attitude measurement scheme and IMM filter tracking method are accurate and simple, and the improvement of the algorithm and the optimization of the model can significantly improve the visual navigation tracking accuracy of the non-cooperative target spacecraft.
【學(xué)位授予單位】：北京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：V448.2;TP391.41

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本文編號(hào)：1549766