无人直升机飞行质量自动检查验收方法及影像处理技术

发布时间：2018-04-09 00:32

本文选题：无人机遥感　切入点：Harris算子　出处：《河南理工大学》2015年硕士论文

【摘要】：由于无人直升机遥感系统受天气影响小、飞行灵活、飞行成本低廉,因此在灾害监测、应急测绘、局部环境监测、区域地形图测制等方面有广泛的应用前景。但无人直升机平台飞行姿态不稳定,载荷有限,多采用非专业小型数码相机,获取影像的畸变差大,像幅小,因此使其影像定位精度和地面影像处理效率受到了一定程度的限制。针对无人直升机遥感系统的这些问题,本文对无人直升机影像特征提取和特征匹配的理论和方法、无人机飞行质量自动检查验收方法及无人机影像畸变校正、几何纠正、DEM提取、正射影像图制作等影像处理技术进行研究,形成了一种基于三角形界限盒的Harris特征匹配方法,构建了无人直升机飞行质量检查验收的技术体系和无人机影像的自动处理流程,并用AF25B无人机影像进行了实验验证。在无人机影像点特征提取和特征匹配方面,引入三角形限界盒思想,设计了一个基于三角形限界盒的Harris特征点匹配算法,并用RANSAC算法剔除误匹配点,形成了一种无人直升机影像自动特征点提取和影像匹配的方法。该方法减少了特征点提取的数量,缩短了特征点提取时间,改善了同名点的匹配精度,为遥感影像的快速精准匹配提供了一种新的技术途径。在深入分析飞行质量检查验收内容和方法的基础上,根据无人机所带载荷的特点和获取的辅助飞行数据,将无人直升机影像飞行质量的检查验收分为有POS数据、有GPS数据和只有影像数据三种情况,设计了不同情况下的检查验收步骤,引入相对定向理论,利用相对定向结果建立影像拓朴关系,进而推导了各种情况下飞行参数的计算公式,形成了一套完善的无人直升机影像飞行质量自动检查验收的方法体系,并用实际影像对该方法的可靠性进行了验证。结果表明本文提出的检查验收方法能快速准确地评估无人直升机的飞行质量,能满足无人机飞行质量检查验收的实际需要。分别对无人直升机影像的畸变校正、几何纠正、DEM提取及正射影像制作等处理技术进行了实验分析,建立了无人机影像处理的一般技术流程,并用实际无人直升机影像进行了实验验证。
[Abstract]:The unmanned helicopter remote sensing system is widely used in disaster monitoring, emergency mapping, local environmental monitoring, regional topographic mapping and so on because of its small weather impact, flexible flight and low flight cost.But the unmanned helicopter platform flight attitude is unstable, the load is limited, most of them use non-professional small digital camera, the distortion of image acquisition is large, the image amplitude is small.Therefore, the accuracy of image location and the efficiency of ground image processing are limited to a certain extent.Aiming at these problems of unmanned helicopter remote sensing system, the theory and method of feature extraction and feature matching of unmanned helicopter image, the automatic inspection and acceptance method of UAV flight quality, distortion correction of UAV image, geometric correction and Dem extraction are discussed in this paper.Based on the research of orthophoto image making and other image processing techniques, a Harris feature matching method based on triangle boundary box is formed, and the technical system of flight quality inspection and acceptance of unmanned helicopter and the automatic processing flow of UAV image are constructed.The AF25B UAV image is used to verify the experiment.In the aspect of feature extraction and feature matching of UAV image points, a Harris feature point matching algorithm based on triangle boundary box is designed, and RANSAC algorithm is used to eliminate mismatch points.An automatic feature point extraction and image matching method for unmanned helicopter images is developed.This method reduces the number of feature points, shortens the time of feature points extraction, improves the matching accuracy of the same name points, and provides a new technical approach for fast and accurate matching of remote sensing images.Based on the in-depth analysis of the contents and methods of flight quality inspection and acceptance, according to the characteristics of the load carried by the UAV and the auxiliary flight data obtained, the inspection and acceptance of the image flight quality of the unmanned helicopter is divided into POS data.In the case of GPS data and only image data, the check and acceptance steps under different conditions are designed, the relative orientation theory is introduced, and the image topologies are established by using the relative orientation results.Furthermore, the calculation formulas of flight parameters under various conditions are derived, and a set of perfect method system for automatic inspection and acceptance of image flight quality of unmanned helicopter is formed, and the reliability of the method is verified with actual images.The results show that the proposed inspection and acceptance method can quickly and accurately evaluate the flight quality of unmanned aerial vehicles, and can meet the actual needs of UAV flight quality inspection and acceptance.The distortion correction, geometric correction Dem extraction and orthophoto image processing techniques of unmanned helicopter image are analyzed experimentally, and the general technical flow of UAV image processing is established.The experimental results are verified by actual unmanned helicopter images.
【学位授予单位】：河南理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：P237

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