无人机自适应姿态角度估计仿真

针对复杂环境下传感器噪声未知且不断变化,会导致姿态融合结果不准确的问题,设计了一种基于单新息自适应算法的卡尔曼滤波器,对加速度计和陀螺仪噪声协方差进行在线估计。首先,介绍了能够结合各个传感器优势的无人机姿态融合算法。然后,设计了采用基于单新息自适应算法的卡尔曼滤波器,给出了能够在线估计加速度噪声协方差R和陀螺仪噪声协方差Q的自适应算法。MATLAB仿真表明单新息自适应卡尔曼滤波器在环境噪声变化时,能够更准确地获得无人机的姿态信息,提高了姿态融合精确度,提高了滤波器的鲁棒性。     

视觉约束下无人机位姿参数的后验估计

针对无人机相对机场跑道的位姿估计问题,提出一种利用机场跑道图像和机载位姿测量传感器进行参数后验估计的方法.首先利用高度表、全球定位系统、惯性导航系统等机载传感器测量无人机的位姿参数.假设各参数的测量误差的先验分布已知且彼此独立,将某时刻的一组测量值看作是对真实值的一次采样.与此同时利用机载已标定相机拍摄跑道的图像,并假设两条跑道边缘直线可从图像中准确提取.受两平行直线透视成像的约束,真实参数必然位于参数空间中某个约束曲面上,进而计算得到位姿参数的后验均值估计.仿真结果验证了该方法的有效性.     

EKF Based Attitude Estimation and Stabilization of a Quadrotor UAV Using Vanishing Points in Catadioptric Images

In recent years, Unmanned Air Vehicles (UAVs) have become more and more important. These vehicles are employed in many applications from military operations to civilian tasks. Under situations where global positioning system (GPS) and inertial navigation system (INS) do not function, or as an additional sensor, computer vision can be used. Having 360° view, catadioptric cameras might be very useful as they can be used as measurement units, obstacle avoidance sensors or navigation planners. Although many innovative research has been done about this camera, employment of such cameras in UAVs is very new. In this paper, we present the use of catadioptric systems in UAVs to estimate vehicle attitude using parallel lines that exist on many structures in an urban environment. After explanation of the algorithm, the UAV modeling and control will be presented. In order to increase the estimation and control speed an Extended Kalman Filter (EKF) and multi-threading are used and speeds up to 40 fps are obtained. Various simulations have been done to present the effectiveness of the estimation algorithms as well as the UAV controllers. A custom test stand has been designed to perform successful experiments on the UAV. Finally, we will present the experiments and the results of the estimation and control algorithms on a real model helicopter. EKF based attitude estimation and stabilization using catadioptric images has found to be a reliable alternative to other sensor usage.     

Vision-aided Estimation of Attitude,Velocity, and Inertial Measurement Bias for UAV Stabilization

This paper studies vision-aided inertial navigation of small-scale unmanned aerial vehicles (UAVs) in GPS-denied environments. The objectives of the navigation system are to firstly online estimate and compensate the unknown inertial measurement biases, secondly provide drift-free velocity and attitude estimates which are crucial for UAV stabilization control, and thirdly give relatively accurate position estimation such that the UAV is able to perform at least a short-term navigation when the GPS signal is not available. For the vision system, we do not presume maps or landmarks of the environment. The vision system should be able to work robustly even given low-resolution images (e.g., 160 ×120 pixels) of near homogeneous visual features. To achieve these objectives, we propose a novel homography-based vision-aided navigation system that adopts four common sensors: a low-cost inertial measurement unit, a downward-looking monocular camera, a barometer, and a compass. The measurements of the sensors are fused by an extended Kalman filter. Based on both analytical and numerical observability analyses of the navigation system, we theoretically verify that the proposed navigation system is able to achieve the navigation objectives. We also show comprehensive simulation and real flight experimental results to verify the effectiveness and robustness of the proposed navigation system.     

基于红外标志灯的无人机视觉编队技术研究

利用视觉进行无人机编队飞行是协同编队飞行中一个新的应用。针对通用的GPS/INS导航系统信号丢失和大国垄断的局限性,提出了一种基于红外标志灯的视觉编队方法,给出了红外LED作为标志灯的可行性分析,并对获取长机位置和姿态信息的LHM算法加以改进,结合最小二乘图像匹配算法的思想提出了LHM-LSIM算法,在图像处理中加以应用,最后通过仿真实验证明该算法的可行性与正确性。     

基于模糊自适应PID的无人机纵向姿态控制研究

针对无人机的典型的非线性、控制参数时变以及建模复杂的特性,设计了一种基于模糊自适应PID的控制器来实现对无人机纵向姿态的控制;该控制器以误差e和误差变化率ec作为输入,可以满足不同时刻e和ec对PID参数自整定的要求;利用模糊控制规则在线对PID参数进行修正,从而使被控对象具有更好的动、静态性能;仿真结果表明,设计的模糊自适应PID控制器具有响应快及超调小的特性,而且自适应能力也较强.     

基于修正的卡尔曼滤波的姿态估计算法研究

研究惯导系统的稳定性问题,其中微惯性测量单元(MIMU)可以为捷联惯导系统提供实时的姿态和航向信息。研究姿态估计提高导航精度,由于陀螺漂移引起姿态误差,单独使用MIMU使姿态精度差。为了克服陀螺误差随时间积累不断增大,无法长时间提供稳定的姿态的缺点,提出采用磁强计修正的卡尔曼滤波四元数姿态估计算法。算法以姿态四元数为状态向量,通过四元数更新方程建立离散滤波状态方程,将加速度计和磁强计输出的六维数据转化为四元数的量测值建立量测方程,有效减少了计算量,补偿陀螺的漂移误差带来的影响。仿真结果表明改进算法提高了捷联惯导系统的精度和稳定性。     

微型UAV视觉识别系统的研究

出于军事侦察、视觉导航等特殊任务的需要,视觉识别系统一直在微型UAV系统中扮演着重要的角色。该文针对微型UAV的特点设计了一套基于机器学习的视觉识别系统。得益于Adaboost算法和层叠分类器的使用,系统具有很快的处理速度并获得了较高的正确识别率。最后,通过一套真实微型无人直升机试验平台上的实验结果,验证了这套系统的可行性。     

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。     

Vision Based Neuro-Fuzzy Controller for a Two Axes Gimbal System with Small UAV

This paper presents the development of a vision-based neuro-fuzzy controller for a two axes gimbal system mounted on a small Unmanned Aerial Vehicle (UAV). The controller uses vision-based object detection as input and generates pan and tilt motion and velocity commands for the gimbal in order to keep the interest object at the center of the image frame. A readial basis function based neuro-fuzzy system and a learning algorithm is developed for the controller to address the dynamic and non-linear characteristics of the gimbal movement. The controller uses two separate, but identical radial basis function networks, one for pan and one for tilt motion of the gimbal. Each system is initialized with a fixed number of neurons that act as rules basis for the fuzzy inference system. The membership functions and rule strengths are then adjusted with the feedback from the visual tracking system. The controller is trained off-line until a desired error level is achieved. Training is then continued on-line to allow the system to accommodate air speed changes. The algorithm learns from the error computed from the detected position of the object in image frame and generates position and velocity commands for the gimbal movement. Several tests including lab tests and actual flight tests of the UAV have been carried out to demonstrate the effectiveness of the controller. Test results show that the controller is able to converge effectively and generate accurate position and velocity commands to keep the object at the center of the image frame.     

基于视觉的导管架入水角估算方法

导管架的入水角对于导管架的滑移入水是一个非常重要的参数.根据海上导管架入水实践,提出一种基于单照相机图像序列的导管架入水角估算方法.估算导管架入水角时,依据导管架上表面的单应矩阵H计算出上表面对应的消影线,然后根据多个单应矩阵计算绝对二次曲线的图像,最后由导管架上表面的消影线、绝对二次曲线的图像以及图像中的海天分隔线估算出导管架的入水角.在模拟实验中对测量误差进行了简要分析,图像实验和真实应用表明该方法是可行的.     

基于计算机视觉的三轴气浮台姿态确定

为实现三轴气浮台室内姿态确定,提出一种基于计算机视觉的姿态确定方案;首先,设计一种以非同心圆对为特征的平面靶标;然后,对平面靶标成像,提取非同心椭圆对;接着,基于二次曲线不变量理论,匹配图像椭圆对和空间圆对;最后,计算平面靶标在摄像机坐标系下的姿态,实现气浮台三轴姿态确定;模拟数据和真实数据试验结果表明,三轴姿态角及光心与平面靶标距离最大相对误差分别小于0.02%和0.008%,算法平均运行时间为12.7ms,可见,提出的视觉确定方案可以实现三轴气浮台姿态精确、实时、鲁棒的测量。     

基于计算机视觉的飞行器姿态快速估计方法

对无人飞行器编队中,队形保持阶段的飞行器姿态估计问题,进行了探索研究和仿真分析;基于计算机视觉中的弱透视投影理论,提出了一种快速的飞行器相对姿态估计方法;利用机载摄像头获得飞行器图像,提取、匹配飞行器图像的三个特征点,用图像中第四点决定姿态变化的方向,实现相对姿态的估计;通过仿真实验分析表明,提出的方法不依赖于特定的飞行器模型,不需要飞行器正面视图;且方法运算快速,计算简单,准确性较高,可以用于飞行器实时姿态估计。     

Robotic Hand Pose Estimation Based on Stereo Vision and GPU-enabled Internal Graphical Simulation

Humanoid robots have complex kinematic chains whose modeling is error prone. If the robot model is not well calibrated, its hand pose cannot be determined precisely from the encoder readings, and this affects reaching and grasping accuracy. In our work, we propose a novel method to simultaneously i) estimate the pose of the robot hand, and ii) calibrate the robot kinematic model. This is achieved by combining stereo vision, proprioception, and a 3D computer graphics model of the robot. Notably, the use of GPU programming allows to perform the estimation and calibration in real time during the execution of arm reaching movements. Proprioceptive information is exploited to generate hypotheses about the visual appearance of the hand in the camera images, using the 3D computer graphics model of the robot that includes both kinematic and texture information. These hypotheses are compared with the actual visual input using particle filtering, to obtain both i) the best estimate of the hand pose and ii) a set of joint offsets to calibrate the kinematics of the robot model. We evaluate two different approaches to estimate the 6D pose of the hand from vision (silhouette segmentation and edges extraction) and show experimentally that the pose estimation error is considerably reduced with respect to the nominal robot model. Moreover, the GPU implementation ensures a performance about 3 times faster than the CPU one, allowing real-time operation.     

Combining Stereo Vision and Inertial Navigation System for a Quad-Rotor UAV

This paper presents the development of a quad-rotor robotic platform equipped with a visual and inertial motion estimation system. Our objective consists of developing a UAV capable of autonomously perform take-off, positioning, navigation and landing in unknown environments. In order to provide accurate estimates of the UAV position and velocity, stereo visual odometry and inertial measurements are fused using a Kalman Filter. Real-time experiments consisting on motion detection and autonomous positioning demonstrate the performance of the robotic platform.     

小型无人机飞行姿态测量系统的设计

飞行姿态是无人机飞行时最重要的参数之一,针对现有多数姿态测量系统体积大、功耗高、续航时间短,并不适合应用于小型或微型无人机上等问题,提出了一种基于DSP的小型化无人机飞行姿态测量的设计方案;完成了微固态陀螺实时数据采集,并通过四阶Runge-Kutta方法对其姿态角进行了求解;给出了姿态测量系统各模块的设计思想及基于μC/OS-Ⅱ的软件设计流程;与NAV440CA-200型IMU对比结果显示,在35秒的测试时间内滚转角相对误差为0.5°,俯仰角相对误差为0.4°;试验结果表明,该姿态测量系统具有较高的测量精度、体积小、功耗低等特点,满足设计需要。