基于模糊预测的INS/视觉无人机自主着陆导航算法

针对 INS/ 视觉无人机自主着陆导航中,由于视觉观测数据具有不确定性噪声及数据更新周期长等原因,易导致卡尔曼滤波估计精度低、实时性差的问题,设计了一种基于模糊预测的INS/ 视觉无人机自主着陆导航算法。首先,利用三角模糊数对视觉观测数据进行处理,减小视觉观测噪声和解算误差的不利影响。然后,在卡尔曼滤波状态方程更新时,采用模糊预测同步预测视觉观测数据,得到无人机高精度姿态信息的同时提高无人机位姿估计的实时性。最后,自主搭建了基于STM32的低成本INS/ 视觉四旋翼无人机自主着陆实验平台,实测飞行着陆结果验证了本文所设计的无人机自主着陆算法可有效避免观测数据存在观测噪声与解算误差,在提高无人机位姿估计精度的同时保证了实时性。     

一种面向移动平台的无人机自主降落控制方法

准确获取无人机的位姿信息是顺利执行无人机自主导航、着陆的首要前提。由于GPS/INS导航系统的局限性和IMU惯性导航系统的误差,提出了一种基于视觉导航的方法。设计了"H"形图像的着陆标志,对机载摄像机采集的实时影像进行图像处理,利用世界坐标系到图像像素坐标系的映射关系得到基于视觉的无人机位姿估计模型,进而解算无人机当前的位姿估计值。上述方法提高了无人机的着陆安全性。仿真结果验证了算法的有效性和位态信息的精确度。     

基于视觉与激光融合的井下灾后救援无人机自主位姿估计

无人机在灾后矿井的自主导航能力是其胜任抢险救灾任务的前提,而在未知三维空间的自主位姿估计技术是无人机自主导航的关键技术之一。目前基于视觉的位姿估计算法由于单目相机无法直接获取三维空间的深度信息且易受井下昏暗光线影响,导致位姿估计尺度模糊和定位性能较差,而基于激光的位姿估计算法由于激光雷达存在视角小、扫描图案不均匀及受限于矿井场景结构特征,导致位姿估计出现错误。针对上述问题,提出了一种基于视觉与激光融合的井下灾后救援无人机自主位姿估计算法。首先,通过井下无人机搭载的单目相机和激光雷达分别获取井下的图像数据和激光点云数据,对每帧矿井图像数据均匀提取ORB特征点,使用激光点云的深度信息对ORB特征点进行深度恢复,通过特征点的帧间匹配实现基于视觉的无人机位姿估计。其次,对每帧井下激光点云数据分别提取特征角点和特征平面点,通过特征点的帧间匹配实现基于激光的无人机位姿估计。然后,将视觉匹配误差函数和激光匹配误差函数置于同一位姿优化函数下,基于视觉与激光融合来估计井下无人机位姿。最后,通过视觉滑动窗口和激光局部地图引入历史帧数据,构建历史帧数据和最新估计位姿之间的误差函数,通过对误差函数的非线性优化...     

无人直升机着陆控制的视觉图像特征处理算法

基于视觉的无人直升机着陆是无人直升机着陆控制中的主要发展方向,视觉图像处理算法将直接影响无人直升机姿态估计以及着陆控制精度.提出了一种应用于无人直升机着陆控制的视觉图像特征处理算法,对目标灰度图像进行阈值处理,边界跟踪,利用凸四边形几何特点提取特征点,最后将所有特征点标记出来.实验结果显示提出的算法即能满足实时处理速度要求,也能满足较高的特征点定位精度要求.这一方法可用于无人直升机着陆控制的相对位置姿态估计.     

一种基于灭影线的无人直升机位姿估计方法

该文将基于灭影线的摄像机标定原理用于无人直升机的位姿估计,设计了一个包含四组平行线的平面着陆图标,通过机载摄像机摄取的图标图像,估计出直升机相对于该图标的位置和姿态。文章首先简述了灭影线的有关知识,推导了相应的关系方程,接着介绍了使用的图像处理方法和提高计算精度的措施,最后,给出了实验结果并对实验误差做了简要分析。该方法的技术关键是如何精确测定灭影点和灭影线的像平面参数,算法最大的特点为计算结果与焦距变化的无关性。实验结果表明,该方法算法复杂性低,实时性好,精度较高,可以用于无人直升机的位姿估计。     

基于摄像头定标的无人机位置和姿态的检测

近年来,无人机技术发展十分迅速,已经被广泛的应用于军事和民用等领域。无人机的回收是无人机整个控制过程中的重中之重。无人机相对机场的方位和三维姿态信息是反映其飞行状态的重要指标,只有精确得到了这些数据信息,才能实现对其正确的稳定的控制,保证无人机安全着陆。随着计算机视觉相关算法性能和可靠性不断提高,完善的实时算法的广泛应用,针对无人机的特点,文章设计了一种基于单目视觉的无人机自主着陆法,完成了无人机空中位姿的判断。实验证明,该方法是可靠和有效的。     

单目视觉系统自运动的滚动时域估计算法

单目视觉系统的自运动估计是计算机视觉领域中的一个关键问题。针对包含有建筑、树木等一般景物特征的应用环境,提出一种单目摄像机位姿估计的滚动时域位姿估计算法。首先分析极线约束方程的不同形式,建立多帧图像闭环之间的时空相关位姿约束,归纳全局最优模型。然后,采用滚动时域方法实现时域窗口内多时刻摄像机位姿的优化估计,实现算法复杂程度和精度的折衷。另外,在室外复杂应用环境下,对常规极约束、冗余极约束和滚动时域冗余极约束这3种位姿估计优化算法进行实验对比,验证该方法的有效性。     

基于双目视觉的小型无人直升机位姿检测

无人直升机的位置与姿态检测是直升机自主飞行控制的基础。基于双目视觉原理,对直升机标志点进行检测并跟踪,利用基于顺序一致性的极线约束匹配方法,对检测到的多个标志点进行立体匹配,计算标志点三维坐标从而得到无人直升机的位置与姿态信息。设计一个基于双目视觉的小型无人直升机空间位姿测量托架,用于飞行控制模型及飞行控制算法的研究及验证。     

考虑多位姿估计约束的双目视觉里程计

为了提升复杂环境中双目视觉里程计的精度,提出一种考虑多位姿估计约束的双目视觉里程计方法.首先,分别建立匹配深度已知点与深度未知点的数学模型,将深度未知点引入2D-2D位姿估计模型,从而充分利用图像信息;然后,基于关键帧地图点改进3D-2D位姿估计模型,并结合当前帧地图点更新关键帧地图点,从而增加匹配点对数,提高位姿估计精度;最后,根据改进的2D-2D及3D-2D位姿估计模型,建立多位姿估计约束位姿估计模型,结合局部光束平差法对位姿估计进行局部优化,达到定位精度高且累积误差小的效果.数据集实验和实际场景在线实验表明,所提出方法满足实时定位要求,且有效地提高了自主定位精度.     

基于UKF的视觉辅助末制导仿真研究

针对巡航导弹中段制导累积误差大的情况,在末制导段采用计算机视觉技术进行辅助制导.介绍了视觉制导的位姿估计方法,并利用无迹卡尔曼滤波器(UKF)对视觉和惯性制导系统进行信息融合,建立了视觉辅助末制导(VATG)的仿真控制模型.实验结果表明:该方法显著提高了末制导系统的精度和可靠性.     

Vision-Based Landing of Light Weight Unmanned Helicopters on a Smart Landing Platform

This paper presents a simple and efficient solution to vision guided autonomous landing of a light-weight (<150 Kg) unmanned helicopter on a smart landing platform, called ISLANDS??Intelligent Self-Leveling and Nodal Docking System. The advantage of ISLANDS is that it may allow the helicopter upon docking to recharge its batteries or refuel, thus, indirectly increasing endurance and flight range. In order for the helicopter to dock with ISLANDS, an on-board ??vision module?? coupled with the helicopter attitude controller is developed. This ??vision module?? detects the location and orientation of ISLANDS and feeds back information to the helicopter attitude controller, which commands the helicopter to descent onto the landing platform at a desired orientation and speed. The Scale Invariant Feature Transform (SIFT) is used for automatic detection of the landing platform based on images captured by a single camera mounted on the helicopter. The detected SIFT features are used to estimate the 3-D orientation of the platform relative to the helicopter using Homography and RANSAC techniques. The focus of this paper is on the vision-guided landing technique in a predefined orientation and not on controller details, which may be found in Shim et al. (1998).     

Using parallel line information for vision-based landmark location estimation and an application to automatic helicopter landing

An approach to landmark location estimation by computer vision techniques is proposed. The objective is to derive the position and the orientation of the landmark with respect to the vehicle by a single image. Such information is necessary for automatic vehicle navigation. This approach requires lower hardware cost and simple computation. The vanishing points of the parallel lines on the landmark are used to detect the landmark orientation. The detected vanishing points are used to derive the relative orientation between the landmark and the camera, which is then utilized to compute the landmark orientation with respect to the vehicle. The size of the landmark is used to determine the landmark position. Sets of collinear points are extracted from the landmark and their inter-point distances are computed. The positions of the collinear point sets are evaluated and used to determine the landmark position. Landing site location estimation by using the identification marking H on the helicopter landing site for automatic helicopter landing is presented as an application of the proposed approach. Simulations and experiments have been conducted to prove the feasibility of the proposed approach.     

Using parallel line information for vision-based landmark location estimation and an application to automatic helicopter landing

An approach to landmark location estimation by computer vision techniques is proposed. The objective is to derive the position and the orientation of the landmark with respect to the vehicle by a single image. Such information is necessary for automatic vehicle navigation. This approach requires lower hardware cost and simple computation. The vanishing points of the parallel lines on the landmark are used to detect the landmark orientation. The detected vanishing points are used to derive the relative orientation between the landmark and the camera, which is then utilized to compute the landmark orientation with respect to the vehicle. The size of the landmark is used to determine the landmark position. Sets of collinear points are extracted from the landmark and their inter-point distances are computed. The positions of the collinear point sets are evaluated and used to determine the landmark position. Landing site location estimation by using the identification marking H on the helicopter landing site for automatic helicopter landing is presented as an application of the proposed approach. Simulations and experiments have been conducted to prove the feasibility of the proposed approach.     

基于仿生双目的无人旋翼机自主着陆方法

基于仿生双目理论提出一种仿双眼异向运动的变视轴夹角双目视觉系统,通过对实时采集的目标图像的处理,研究其双目摄像机夹角的在线自标定,实现获取无人旋翼机的实时高度信息。实验结果表明,该方法获得的光轴夹角信息相对误差小于5%,能准确控制双目摄像机的转动,避免由于双目视觉盲区所造成的着陆误差,系统所标定出的旋翼机高度信息也为其安全着陆提供了有力保障。     

Camera position estimation and feature extraction from an incomplete image of a landmark

In the autonomous unmanned helicopter landing problem, the position of the unmanned helicopter relative to the landmark is very important. A camera carried on the unmanned helicopter can capture an image of the landmark. In earlier research, it was reported that the camera position could be estimated by features extracted from the landmark image. However, it is necessary that the landmark image should be complete, or with only slight deficiencies, in order for this estimation process to be possible. In this article, we report on an innovative design for an estimation made from a camera position giving an incomplete single image of the landmark. An adaptive neuro-fuzzy inference system (ANFIS) is used to construct the mapping relation between the features of complete and incomplete landmark images. It will be verified that it is possible to estimate the camera position from a landmark image more than half of which is defective via the proposed method.     

Autonomous Landing of Small Unmanned Aerial Rotorcraft Based on Monocular Vision in GPS-denied Area

Focusing on the low-precision attitude of a current small unmanned aerial rotorcraft at the landing stage, the present paper proposes a new attitude control method for the GPS-denied scenario based on the monocular vision. Primarily, a robust landmark detection technique is developed which leverages the well-documented merits of supporting vector machines (SVMs) to enable landmark detection. Then an algorithm of nonlinear optimization based on Newton iteration method for the attitude and position of camera is put forward to reduce the projection error and get an optimized solution. By introducing the wavelet analysis into the adaptive Kalman filter, the high frequency noise of vision is filtered out successfully. At last, automatic landing tests are performed to verify the method's feasibility and effectiveness.      

无人直升机自主着舰的目标定位视觉算法

根据无人直升机自主着舰的任务特点,构造了用于无人直升机自主着舰的视觉系统,选用仿射不变矩作为该视觉系统目标识别的特征和信息提取的手段,仿真实验表明,开发的算法能够在纵摇角小于15°的情况下正确的识别出目标,对目标中心位置的估计偏差在每个坐标轴向小于3个像素,方位角估计偏差小于9°。整套算法的鲁棒性和实时性满足无人直升机自主着舰的需求。     

A Quadrotor Test Bench for Six Degree of Freedom Flight

This paper focuses on the system identification of a small unmanned helicopter in hover or low-speed flight conditions. A novel genetic algorithm including chaotic optimization operation named chaos-genetic algorithm (CGA) is proposed to identify the linear helicopter model. Based on the input-output data collected from real flight tests, the identification performance of CGA is compared with those calculated by the traditional genetic algorithm (TGA) and the prediction error method (PEM). The accuracy of the identified model is verified by simulation in time domain. Additionally, the small unmanned helicopter is stabilized by a linear quadratic Gaussian (LQG) regulator based on the proposed identified model. In the automatic flight experiments, the achievement of automatic take-off and landing, hovering performance within a 1.2?m diameter circle and point-to-point horizontal polyline flight also demonstrates the accuracy of the identified model and the effectiveness of the proposed method.     

Development of a vision-based ground target detection and tracking system for a small unmanned helicopter

It is undoubted that the latest trend in the unmanned aerial vehicles (UAVs) community is towards visionbased unmanned small-scale helicopter, utilizing the maneuvering capabilities of the helicopter and the rich information of visual sensors, in order to arrive at a versatile platform for a variety of applications such as navigation, surveillance, tracking, etc. In this paper, we present the development of a visionbased ground target detection and tracking system for a small UAV helicopter. More specifically, we propose a real-time vision algorithm, based on moment invariants and two-stage pattern recognition, to achieve automatic ground target detection. In the proposed algorithm, the key geometry features of the target are extracted to detect and identify the target. Simultaneously, a Kalman filter is used to estimate and predict the position of the target, referred to as dynamic features, based on its motion model. These dynamic features are then combined with geometry features to identify the target in the second-stage of pattern recognition, when geometry features of the target change significantly due to noise and disturbance in the environment. Once the target is identified, an automatic control scheme is utilized to control the pan/tilt visual mechanism mounted on the helicopter such that the identified target is to be tracked at the center of the captured images. Experimental results based on images captured by the small-scale unmanned helicopter, SheLion, in actual flight tests demonstrate the effectiveness and robustness of the overall system.