本文關(guān)鍵詞： 無(wú)人機(jī) 攝影測(cè)量 自駕儀 MEMS陀螺 MEMS加速度計(jì) 捷聯(lián)慣性導(dǎo)航 組合導(dǎo)航 Kalman濾波 Fuzzy-PID 飛行控制律 自穩(wěn)定平臺(tái) 數(shù)字相機(jī)檢定 DLT 多片后交 六旋翼　出處：《解放軍信息工程大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：隨著現(xiàn)代遙感技術(shù)的發(fā)展,測(cè)繪、土地、電力、公安、城建等部門對(duì)局部區(qū)域高空間分辨率、高光譜分辨率和高時(shí)間分辨率遙感產(chǎn)品的要求越來(lái)越迫切,測(cè)繪型無(wú)人機(jī)平臺(tái)作為新型的遙感平臺(tái)以其使用靈活方便,基本不受場(chǎng)地和天氣的限制,可攜帶各種有效載荷,成本低廉等優(yōu)點(diǎn),已漸漸成為民用無(wú)人機(jī)應(yīng)用研究的熱點(diǎn)。本文主要圍繞無(wú)人機(jī)遙感系統(tǒng)關(guān)鍵技術(shù)展開研究,重點(diǎn)對(duì)自動(dòng)駕駛儀、組合導(dǎo)航算法、三軸自穩(wěn)技術(shù)、相機(jī)檢定技術(shù)進(jìn)行研究,通過(guò)綜合成圖實(shí)驗(yàn)與精度驗(yàn)證實(shí)驗(yàn)證明本文所述方法的正確性。主要工作和創(chuàng)新點(diǎn)如下:1)為滿足測(cè)繪型無(wú)人機(jī)對(duì)基于多傳感器捷聯(lián)慣導(dǎo)的多源導(dǎo)航數(shù)據(jù)信息融合、基于模糊控制的區(qū)域任務(wù)飛行和三軸穩(wěn)定平臺(tái)三個(gè)關(guān)鍵技術(shù)對(duì)硬件系統(tǒng)的要求,完成了小型自動(dòng)駕駛儀系統(tǒng)的整體架構(gòu)設(shè)計(jì)、電路參數(shù)計(jì)算、元器件選型以及原型機(jī)的制作等工作。2)在充分分析捷聯(lián)慣性導(dǎo)航(SINS,Strapdown Inertial Navigation System)、全球衛(wèi)星導(dǎo)航(GNSS,Global Navigation Satellite System)、大氣導(dǎo)航和磁導(dǎo)航的原理基礎(chǔ)上,重點(diǎn)推導(dǎo)了符合自制自駕儀硬件方案的集中式卡爾曼濾波(Kalman)濾波的實(shí)用組合導(dǎo)航算法。仿真實(shí)驗(yàn)結(jié)果表明,組合導(dǎo)航較獨(dú)立導(dǎo)航系統(tǒng)在改善系統(tǒng)穩(wěn)定性和提高精度方面有明顯的優(yōu)勢(shì),濾波后輸出的航向角精度為0.28°(動(dòng)態(tài)范圍10°),橫滾角精度為0.39°(動(dòng)態(tài)范圍10°),俯仰角度精度為0.77°(動(dòng)態(tài)范圍40°),水平精度可為2.25m,高度精度可達(dá)0.7m。3)依據(jù)航空攝影測(cè)量規(guī)范對(duì)飛行平臺(tái)和有效載荷的要求,系統(tǒng)地提出了無(wú)人機(jī)飛控和三軸自穩(wěn)定云臺(tái)的控制精度指標(biāo),設(shè)計(jì)并實(shí)現(xiàn)了無(wú)人機(jī)飛控和三軸自穩(wěn)定云臺(tái)的Fuzzy-PID控制算法。實(shí)驗(yàn)證明,Fuzzy-PID比傳統(tǒng)PID在響應(yīng)時(shí)間、失調(diào)量、穩(wěn)定時(shí)間和穩(wěn)態(tài)誤差上均優(yōu)于傳統(tǒng)PID,尤其超調(diào)量特性非常有利于無(wú)人機(jī)的穩(wěn)定控制。Fuzzy-PID的上升時(shí)間為0.13s比傳統(tǒng)PID慢0.05s,但在可接受范圍內(nèi)。室內(nèi)精度實(shí)驗(yàn)和實(shí)際飛行實(shí)驗(yàn)檢測(cè)表明各項(xiàng)指標(biāo)達(dá)到或優(yōu)于設(shè)計(jì)要求,滿足小區(qū)域大比例尺地形圖立體測(cè)繪對(duì)飛控和穩(wěn)定平臺(tái)的要求。4)針對(duì)非量測(cè)型CCD數(shù)字相機(jī)特點(diǎn)和野外快速檢定非量測(cè)型相機(jī)的現(xiàn)實(shí)需求,詳細(xì)分析了誤差來(lái)源,建立了相機(jī)檢定的數(shù)學(xué)模型。詳細(xì)討論了多片分組迭代求解DLT系數(shù)、內(nèi)方位和畸變參數(shù)和多片后方交會(huì)法求解內(nèi)外方位和畸變參數(shù)的方法,自制了野外快速非量測(cè)型相機(jī)的檢定架,通過(guò)檢定片分別求出兩種方法下的內(nèi)方位和畸變參數(shù),通過(guò)驗(yàn)證片進(jìn)行了精度驗(yàn)證。從精度驗(yàn)證片前方交會(huì)的結(jié)果來(lái)看,多片DLT算法的X最大誤差為0.2585mm,Y最大誤差為0.6719mm,Z最大誤差為0.1319mm,多片后交算法的X最大誤差為0.1914mm,Y最大誤差為0.9808mm,Z最大誤差為0.1453mm。2種方法的前方交會(huì)精度相當(dāng),均小于1mm;多片DLT算法平面精度小于0.2585mm,高程精度小于0.6719mm,多片后交算法平面精度小于0.1914mm,高程精度小于0.9808mm,多片后交算法平面精度略高于多片DLT,而多片DLT算法的高程精度好于多片后交算法。本文采取的兩種多片相機(jī)檢定的方法,都能夠基本滿足非量測(cè)型相機(jī)用于攝影測(cè)量的要求,同時(shí)也注意到了內(nèi)方位元素和畸變差參數(shù)解算精度不高的問(wèn)題。但是對(duì)于非量測(cè)相機(jī)來(lái)說(shuō),內(nèi)方位和物鏡畸變差參數(shù)真值的未知并不影響攝影測(cè)量的精度。
[Abstract]:With the development of modern remote sensing technology, surveying and mapping, land, power, public security, urban construction and other departments of the local area of high spatial resolution, high spectral resolution and high temporal resolution remote sensing products is more and more urgent, mapping the UAV platform as a new remote sensing platform with its flexible and convenient use, no space and weather restrictions that can carry a variety of payloads, low cost and other advantages, has gradually become a hot topic of civilian UAV applications. This paper mainly focuses on the key technology of UAV remote sensing system is studied, focusing on autopilot, navigation algorithm, three axis stabilization technology, research on camera calibration technology, through the integrated into the correct map experiment and accuracy verification experiment proves that the method in this paper. The main work and innovation are as follows: 1) to meet the unpiloted mapping of multiple sensors based on agile strapdown navigation multi Guide Navigation information fusion, fuzzy control of the regional flight mission requirements and three axis stabilized platform three key technologies of hardware system based on the completion of the overall architecture of the autopilot system design, circuit parameter calculation, component selection and prototype manufacture,.2) in the full analysis of strapdown inertial navigation (SINS, Strapdown Inertial Navigation System (GNSS), global satellite navigation, Global Navigation Satellite System), the basic principle of air navigation and magnetic navigation, the key is derived with self centralized driving Calman filter instrument hardware scheme (Kalman) practical navigation algorithm filter. Simulation results show that the integrated navigation and navigation system is significantly more independent the advantage in improving the stability of the system and improve the accuracy of the filtered output precision of yaw angle is 0.28 degrees (dynamic range 10 degrees), roll angle precision 0.39 degrees (dynamic range of 10 DEG), pitch angle accuracy is 0.77 degrees (dynamic range of 40 DEG), the level of accuracy is 2.25m, the height accuracy of 0.7m.3) on the basis of aerial photogrammetric specification of flight platform and payload requirements, systematically put forward the index control precision of UAV flight control and three axis stabilized platform the design and implementation of Fuzzy-PID control algorithm for UAV flight control and three axis self stabilizing PTZ. The experimental results show that the misalignment Fuzzy-PID than the traditional PID in the response time, stable time and steady error is superior to the traditional PID, especially the overshoot characteristics of UAV is very conducive to the stability control of.Fuzzy-PID rise time 0.13s compared with the traditional PID slow 0.05s, but in the acceptable range. The accuracy of indoor experiment and practical flight test shows that all indexes reached or exceeded the design requirements, meet the area of large scale topographic map of stereo mapping on the fly Control and stable platform for.4) for non metric digital camera CCD characteristics and field verification non realistic demand measurement camera, a detailed analysis of the error sources, establishes the mathematical model of camera calibration. Multi block iterative DLT coefficients is discussed in detail, and in the range of distortion parameters and multi piece rear the intersection method of solving inside and outside orientation and distortion parameters, made the verification frame rapid field non metric camera, through the verification sheet were obtained by the two methods within the range and distortion parameters, through the verification sheet were verified. Verification sheet intersection results from the precision, the maximum error of more than X DLT algorithm for 0.2585mm Y, the maximum error is 0.6719mm, the maximum error is Z 0.1319mm X, the maximum error algorithm to multi chip after 0.1914mm Y, the maximum error is 0.9808mm, the maximum error is Z 0.1453mm.2 method of intersection The accuracy is less than 1mm; multi DLT algorithm of plane precision is less than 0.2585mm, the elevation accuracy is less than 0.6719mm, multi piece after the plane algorithm precision is less than 0.1914mm, the elevation accuracy is less than 0.9808mm, multi piece after the algorithm is slightly higher than the plane precision of multi DLT and multi DLT algorithm, high precision in multi process after algorithm. This article adopts the method of two kinds of multi camera calibration, can basically meet the non metric camera is used in photography measurement, but also pays attention to the inner orientation elements and the distortion parameters calculation accuracy is not high. But for non metric camera, and lens distortion parameters within the range the true value of the unknown does not affect the photographic measurement accuracy.

【學(xué)位授予單位】：解放軍信息工程大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：P237
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本文編號(hào)：1456535