Airborne Vision-Based Navigation Method for UAV Accuracy Landing Using Infrared Lamps

In this paper, an airborne vision-based navigation method for Unmanned Aerial Vehicle (UAV) accuracy landing is presented. In this method, a visible light camera integrated with a Digital Signal Processing (DSP) processor is installed on the UAV and a 940 nm optical filter is fixed in front of the camera lens. In addition, four infrared light-emitting diode (LED) lamps whose emission wavelengths are 940 nm are placed behind ideal landing site on the runway. In this way, the infrared lamps in the image are distinct even if the image background is complicated. In the image processing procedure, firstly maximum between-class variance algorithm and region growing algorithm are used to determine candidate infrared lamp regions in the images. Then Negative Laplacian of Gaussian (NLOG) operator is applied to detect and track centers of the infrared lamps in the images. The space position and attitude of the camera can be obtained according to the corresponding relationship between image coordinates and space coordinates of the infrared lamp centers. Finally, high precision space position of the UAV can be calculated according to the installation relationship between the camera and the UAV. Plenty of real flight and static precision experiments have proved the validity and accuracy of the proposed method.     

基于DWILS的无人机着陆导引与控制仿真

导引和控制技术是大型无人机实现自动着陆的两项关键技术。针对分米波仪表着陆系统（DWILS）的无人机着陆导引与控制技术的仿真验证,为了提高导引的可靠性,给出了基于DWILS的无人机着陆地面仿真系统的结构,着陆导引仿真设备,根据DWILS的工作原理给出了导引信号的计算公式,同时设计了着陆控制器。根据导引设备的原理建立了基于DWILS的无人机着陆导引与控制仿真系统模型,并进行仿真验证。仿真结果表明,基于DWILS的导引方式满足无人机着陆导引的需要,设计的着陆控制器具有较好的控制效果。     

无人机软式自主空中加油视觉导航方法

针对无人机软式自主空中加油问题,提出了用于无人机软式自主空中加油的视觉导航方法.在加油锥套外环平面上均匀布置8个近红外LED,在受油机上布置CCD相机及带通滤镜构成视觉系统,利用该系统对锥套进行近红外成像,对所获取的近红外图像进行特征点提取并利用迭代算法估计锥套与受油机之间的位姿参数.在不同距离下对该视觉导航方法进行了仿真验证.结果表明,所研究的无人机软式自主空中加油视觉导航方法精度较高,抗干扰能力较强,可以有效地用于无人机软式自主空中加油.     

Autonomous Vision-based Target Detection and Safe Landing for UAV

International Journal of Control, Automation and Systems - Target detection is crucial for many applications of Unmanned Aerial Vehicles (UAVs) such as search and rescue, object transportation,...     

Fusion of Data and Knowledge for Safe UAV Landing

Autonomous Unmanned Aerial Vehicles (UAVs) have the potential to significantly improve current working practices for a variety of applications including aerial surveillance and search-and-rescue. However before UAVs can be widely integrated into civilian airspace there are a number of technical challenges which must be overcome including provision of an autonomous method of landing which would be executed in the event of an emergency. A fundamental component of autonomous landing is safe landing zone detection of which terrain classification is a major constituent. Presented in this paper is an extension of the Multi-Modal Expectation Maximization algorithm which combines data in the form of multiple images of the same scene, with knowledge in the form of historic training data and Ordnance Survey map information to compute updated class parameters. These updated parameters are subsequently used to classify the terrain of an area based on the pixel data contained within the images. An image''s contribution to the classification of an area is then apportioned according to its coverage of that area. Preliminary results are presented based on aerial imagery of the Antrim Plateau region in Northern Ireland which indicates potential in the approach used.     

基于视觉的无人机板载自主实时精确着陆系统

传统视觉方案无法应对无人机降落过程中复杂的环境变化,难以实现在机载处理器上的实时图像处理.为此,提出一种适用于无人机板载端轻量高效的Onboard-YOLO算法,使用可分离卷积代替常规卷积核提升计算速度,通过注意力机制自动学习通道特征权重提高模型准确度.在运动模糊、遮挡、目标出视野、光照、尺度变化等5种干扰环境下进行降...     

A Vision-Based Automatic Landing Method for Fixed-Wing UAVs

In this paper, a vision-based landing system for small-size fixed-wing unmanned aerial vehicles (UAVs) is presented. Since a single GPS without a differential correction typically provide position accuracy of at most a few meters, an airplane equipped with a single GPS only is not guaranteed to land at a designated location with a sufficient accuracy. Therefore, a visual servoing algorithm is proposed to improve the accuracy of landing. In this scheme, the airplane is controlled to fly into the visual marker by directly feeding back the pitch and yaw deviation angles sensed by the forward-looking camera during the terminal landing phase. The visual marker is a monotone hemispherical airbag, which serves as the arresting device while providing a strong and yet passive visual cue for the vision system. The airbag is detected by using color- and moment-based target detection methods. The proposed idea was tested in a series of experiments using a blended wing-body airplane and proven to be viable for landing of small fixed-wing UAVs.     

Landmark Selection for Vision-Based Navigation

Recent work in the object recognition community has yielded a class of interest-point-based features that are stable under significant changes in scale, viewpoint, and illumination, making them ideally suited to landmark-based navigation. Although many such features may be visible in a given view of the robot's environment, only a few such features are necessary to estimate the robot's position and orientation. In this paper, we address the problem of automatically selecting, from the entire set of features visible in the robot's environment, the minimum (optimal) set by which the robot can navigate its environment. Specifically, we decompose the world into a small number of maximally sized regions, such that at each position in a given region, the same small set of features is visible. We introduce a novel graph theoretic formulation of the problem, and prove that it is NP-complete. Next, we introduce a number of approximation algorithms and evaluate them on both synthetic and real data. Finally, we use the decompositions from the real image data to measure the localization performance versus the undecomposed map.     

Visual-based Landing Guidance System of UAV with Deep Learning Technique for Environments of Visual-detection Impairment

International Journal of Control, Automation and Systems - Most vision-based landing algorithms cannot be applied in a severe vision-detection environment. However, the application of a deep...     

雷达性能对无人机着陆引导精度的影响

在无人机着陆精度优化的过程中,延迟和跳点是雷达引导无人机着陆过程中常见的问题.针对上述问题,建立了雷达引导无人机着陆的纵向运动模型,并在此基础上开展了研究.得出了延迟效应、跳点强度和跳点数量对无人机着陆引导精度的影响,并对仿真结果做出了理论分析,提出了工程上可行的解决方案.上述研究有利于了解影响雷达对无人机着陆误差的原因,为提高雷达的设计性能提供了理论依据和解决问题的方案.     

Relative Navigation Approach for Vision-Based Aerial GPS-Denied Navigation

GPS-denied aerial flight is a challenging research problem and requires knowledge of complex elements from several distinct disciplines. Additionally, aerial vehicles can present challenging constraints such as stringent payload limits and fast vehicle dynamics. In this paper we propose a new architecture to simplify some of the challenges that constrain GPS-denied aerial flight. At the core, the approach combines visual graph-SLAM with a multiplicative extended Kalman filter. More importantly, for the front end we depart from the common practice of estimating global states and instead keep the position and yaw states of the MEKF relative to the current node in the map. This relative navigation approach provides simple application of sensor measurement updates, intuitive definition of map edges and covariances, and the flexibility of using a globally consistent map when desired. We verify the approach with hardware flight-test results.     

基于计算机视觉的车辆辅助导航定位方法

为了将大型车辆准确地将大型车辆停放到某一位置,论文提出了一种基于计算机视觉的车辆辅助导航定位方式.通过隔行累加基础上的Hough变换检测出导航标尺的位置,分析了车辆导航定位所需的信息量,并根据导航标尺给驾驶员实时提供角度和位置信息.试验表明,该方法不仅结构简单,便于硬件实现,而且实时性好.     

基于场景识别的无人机自主着陆组合导航研究

研究无人机自主着陆阶段的组合导航问题,现有绝大多数是GPS/INS的组合导航,其中状态量的位置信息也都选择了传统的纬度、经度和海拔高度.为了适应着陆场景识别导航,提出了一种基于场景识别/INS的无人机自主着陆组合导航新方法.从机场原点指向飞机的矢量相对机场的俯仰角、方位角及矢量长度作为卡尔曼滤波状态量的位置信息,组合导航中量测信息是从飞机上的摄像机所获得的机场特征点在摄像机所摄图像中的位置(坐标).通过对方法的分析及仿真,结果验证了方法在无人机自主着陆阶段组合导航中,稳定性和快速性等识别性能较好.     

基于多层级合作标识无人机自主着舰视觉引导方法

无人机自主着舰视觉引导应用距离跨度大、测量精度要求高,尚缺乏有效的解决方法,针对此问题,提出一种基于多层级合作标识的无人机自主着舰视觉引导方法。该方法首先设计了基于ArUco的多层级组合新型合作标识,利用边缘检测算法对每一层ArUco二维码提取控制点,进一步通过求解PnP(Perspective-n-Point)问题得到机舰间相对位姿信息,然后对每一层二维码所解算出的位姿数据进行非线性误差补偿,利用最小二乘法(Least Mean Squares,LMS)与熵权法解算不同层级二维码的权值大小,最终通过融合不同层级二维码位姿解算结果得到最终的位姿信息,实现大距离跨度范围内的高精度位姿求解。实验结果表明,本文方法实现了大距离跨度范围内机舰间相对位姿参数的高精度测量,可为无人机自主着舰提供可靠引导信息。     

基于MapX开发的无人机导航系统

使用MapX在无人机导航系统中添加能够处理矢量地图数据的地理信息模块,可以显著地提升系统的性能;文章对如何在矢量地图下进行无人机导航与任务规划进行了研究,完成对地理信息系统中矢量地图的基本控制和操作,并在地理信息图上进行航线的设定与修改;另外,针对二维矢量地图无法对无人机进行三维空间定位的问题,文章通过在无人机导航系统中添加高度位置显示模块,用二维平面地图与高度图相结合的方式来实现对无人机位置的三维定位;通过在无人机仿真实验系统中的使用,证明了该系统能高效、直观地对无人机飞行进行监视与控制。     

Vision-Based Fixed-Time Uncooperative Aerial Target Tracking for UAV

<正>Dear Editor, In this letter, a vision-based fixed-time control is proposed for an unmanned aerial vehicle(UAV) with actuator saturation to track an uncooperative target. The fixed-time control is designed in backstepping framework. The relative states between UAV and the target are not directly measured, and are estimated by an onboard monocular camera.     

无人机视觉自主着陆仿真验证系统时间延迟测定研究

介绍了无人机视觉自主着陆仿真验证系统,无人机视觉自主着陆半实物仿真试验一般采用视景生成平台与实时监控仿真平台组成的双机闭环仿真系统结构,由于此种双机结构,两平台之间的数据交换产生的实时同步问题对半实物仿真试验的可信度和精度都会造成不良影响;针对实时同步问题的产生机理,采用RTX高精度时间采集和数字图像识别技术,构造了一种对系统时间延迟的测定方法;实验证明方法可行,为解决实时同步问题提供依据,提高了半实物仿真试验的可信度和精度.     

无人机微波着陆引导系统坐标变换算法研究实现

为了实时且精确地获取飞机相对于着陆点的位置坐标,在深入分析机场场地位置几何信息和地面设备制导信息并建立坐标变换测量方程的基础上,提出了一种基于坐标系旋转的旋转坐标迭代解算算法;该算法通过坐标系的旋转消除了飞机纵轴方向上的位移,而后研究其具体迭代解算过程,并通过仿真进行算法性能分析;将所提出的旋转坐标迭代解算算法在TMS320F2812DSP上进行实现,并利用集成开发环境(CCS)进行调试与仿真;结果表明:旋转坐标迭代解算算法收敛快速且精确度高,DSP实现的旋转坐标迭代解算算法所得坐标值与软件仿真结果基本一致,满足系统性能指标要求,对于无人机着陆制导信息的获取具有实用价值。     

基于计算机视觉/INS的无人机自主着陆组合导航研究

提出了一种基于单目视觉/INS组合导航的无人机自主着陆末端导航信息提取的新方法;它只需要检测跑道上的两个特征点再结合?导系统的姿态角信息,就可以直接解算出飞机相对于机场的位置矢量;由于直线具有明显的可观性,所以对于特征点较难提取的可以通过对两条跑道边缘线和一条着陆阈值线进行检测,然后取阈值线与跑道边缘线的交点作为特征点;依据成像的几何原理,对算法进行了详细的推导,最后通过着陆实验表明该方法的提取与转换是切实可行的。     

景象匹配导航中的航路点自动提取算法

针对景象匹配导航中适配区提取算法选取适配区时存在匹配精度低、速度慢的问题,提出了一种航路点自动提取算法.利用图像灰度信息、边缘信息和图像间互相关信息,对航拍图像中不同尺度图像样本进行参数训练,进而得到各尺度下的分类参数,并通过支持向量机完成航路点区域的自动粗分类.针对航路点选取过于密集导致选取的航路点不唯一及计算量增加的问题,在欧式距离分类法基础上,提出了多分类无监督聚类算法,从而实现航路点的优化.仿真和航拍数据结果表明改进的算法可以自动提取航路点,并且提取的航路点具有信息量丰富、稳定性好、匹配精度高及速度快等优点.