本文關(guān)鍵詞： 無人機(jī) 自主著陸 視覺導(dǎo)航 卡爾曼濾波　出處：《西安電子科技大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：無人機(jī)體積小、成本低、能適應(yīng)各種工作環(huán)境的特點(diǎn)使其廣泛的應(yīng)用于各個(gè)領(lǐng)域中,隨著軍用、民用以及商用的需求越來越大,關(guān)于無人機(jī)的研究有了顯著的發(fā)展。在無人機(jī)著陸導(dǎo)航過程中,傳統(tǒng)的慣導(dǎo)系統(tǒng)存在精度低、易受電子干擾等缺陷,基于機(jī)器視覺的導(dǎo)航方法越來越受到重視。本文著重研究了基于機(jī)器視覺的自主著陸算法,并通過無人機(jī)實(shí)地試驗(yàn)對(duì)算法的可用性作了驗(yàn)證,通過三維視景仿真對(duì)算法的準(zhǔn)確性作了驗(yàn)證。并且將無損卡爾曼濾波應(yīng)用于算法中,提高系統(tǒng)的精度和穩(wěn)定性。首先,文章描述了無人機(jī)視覺導(dǎo)航系統(tǒng)的設(shè)計(jì),著重研究了“姿態(tài)及位置確定”階段,這一階段分為圖像處理模塊和位姿估計(jì)模塊,圖像處理模塊中,主要對(duì)圖像進(jìn)行了預(yù)處理、邊緣檢測(cè)、輪廓匹配等操作,確定找到合作地標(biāo)后再使用光流法進(jìn)行目標(biāo)跟蹤,以此方法來提高算法的效率和實(shí)時(shí)性。其次,文章研究了“姿態(tài)及位置確定”階段中的第二個(gè)模塊,即無人機(jī)視覺導(dǎo)航控制中的位姿估計(jì)模型。根據(jù)透視投影模型和坐標(biāo)系相互之間的轉(zhuǎn)換關(guān)系,建立一個(gè)數(shù)學(xué)模型,再加上實(shí)驗(yàn)獲得有效特征點(diǎn)信息,就可以依據(jù)這個(gè)數(shù)學(xué)模型,可以推導(dǎo)出一個(gè)六分量的超定方程組,并使用奇異值分解的方法求解此方程組,并使用最小二乘法的方法估計(jì)出位姿信息的最優(yōu)解。然后,文章使用大疆六旋翼無人機(jī)實(shí)踐了自主著陸實(shí)地試驗(yàn),操控?zé)o人機(jī)模擬飛行降落過程,將獲得的視頻資料用于設(shè)計(jì)的程序中,結(jié)果輸出了一系列的位姿數(shù)據(jù),即驗(yàn)證了算法的可執(zhí)行性。并介紹了合成視景三維建模的一般流程,設(shè)計(jì)了一個(gè)經(jīng)典算法,在理想的三維建模環(huán)境下,將無人機(jī)自身的位姿信息和實(shí)驗(yàn)所得位姿信息進(jìn)行比對(duì),誤差在允許范圍內(nèi),成功得驗(yàn)證了論文算法的正確性。最后,文章研究了無損卡爾曼濾波算法在無人機(jī)定位中的應(yīng)用,包括異源傳感器數(shù)據(jù)融合、和數(shù)據(jù)預(yù)估計(jì),并且將算法設(shè)計(jì)采用MATLAB軟件進(jìn)行了仿真,結(jié)果證明卡爾曼濾波可以使位姿估計(jì)更加精確,系統(tǒng)更加穩(wěn)定。
[Abstract]:Unmanned aerial vehicles (UAVs) are widely used in various fields because of their small size and low cost. With the increasing demand for military, civilian and commercial applications, UAVs can adapt to the characteristics of various working environments. The research on UAV has made remarkable progress. In the course of UAV landing navigation, the traditional inertial navigation system has some defects, such as low precision, easy to be interfered by electronic, etc. More and more attention has been paid to the navigation method based on machine vision. This paper focuses on the autonomous landing algorithm based on machine vision, and verifies the usability of the algorithm through the field test of UAV. The accuracy of the algorithm is verified by 3D visual simulation, and the lossless Kalman filter is applied to the algorithm to improve the accuracy and stability of the system. Firstly, the design of the vision navigation system for UAV is described. This stage is divided into image processing module and pose estimation module. In the image processing module, image preprocessing, edge detection, contour matching and other operations are mainly carried out in the image processing module. In order to improve the efficiency and real-time performance of the algorithm, optical flow method is used to track targets after finding cooperative landmarks. Secondly, the second module in the phase of "attitude and position determination" is studied in this paper. According to the transformation relationship between perspective projection model and coordinate system, a mathematical model is established, which can be based on the effective feature point information obtained by experiments, according to the transformation relationship between perspective projection model and coordinate system. A six-component system of overdetermined equations can be derived, and the singular value decomposition method is used to solve the equations, and the least square method is used to estimate the optimal solution of the position and attitude information. In this paper, an autonomous landing field experiment is carried out with DJ6 rotor-wing UAV, and the UAV is operated to simulate the flight landing process. The obtained video data are used in the design program, and a series of position and pose data are output. The algorithm is proved to be executable, and the general flow of 3D modeling of synthetic scene is introduced. A classical algorithm is designed to compare the position and pose information of UAV with that obtained from experiments in an ideal 3D modeling environment. The error is within the allowable range, and the correctness of the algorithm is verified successfully. Finally, the application of lossless Kalman filter algorithm in UAV positioning is studied, including data fusion of heterogenous sensors and data pre-estimation. The algorithm is simulated with MATLAB software. The results show that Kalman filter can make the position and pose estimation more accurate and the system more stable.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：V279

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