基于光流和惯性导航的小型无人机定位方法

为了提高小型无人机定位精度,提出了一种基于光流系统和惯性导航系统相结合的定位方法.在介绍光流传感器工作原理,建立光流数学模型和分析光流测量误差之后,提出了采用双光流传感器测量飞行器的高度和速度,并将二维光流信息与捷联惯性导航系统(SINS)通过扩展卡尔曼滤波器(EKF)进行数据融合,得到实时的位置、速度和姿态.室内实验结果表明:该方案能够有效地减小导航中的位置、速度和姿态误差,提高定位精度.     

昆虫视觉启发的光流复合导航方法

昆虫能够使用视觉感受的光流(Optical flow, OF)信息执行导航任务. 启发于昆虫的视觉导航, 本文提出了一种生物视觉启发的光流复合导航方法, 它由光流导航和光流辅助导航两部分组成, 以实现高效精确的视觉导航定位. 该方法中, 光流导航的作用是使用昆虫视觉启发的光流法, 测量系统每一时刻的运动位移, 然后使用路径积分累加位移得到位置信息; 光流辅助导航的作用是针对路径积分会产生累积误差的缺点, 使用光流匹配的方法来估计和修正导航中的位置误差. 该光流辅助导航也参考了昆虫启发的光流法, 通过基于光流的卡尔曼滤波器来执行实际和预测光流的迭代匹配. 由于光流导航和光流辅助导航中的光流计算来源于同一昆虫启发光流法, 使得光流复合导航的两部分可共享输入信号和部分执行过程. 文中使用移动机器人进行导航实验,证明了该复合导航方法的效率.     

偏振光辅助定姿在基于光流自主导航中的应用

根据大气偏振光分布特点和偏振光导航原理搭建三通道测量平台对偏振光信息进行采集,通过视觉图像特征的空间梯度瞬时变化率计算飞行器周围环境的稀疏光流场.结合大气偏振光与运动环境光流信息构造基于 GPS/惯导/偏振光/光流组合导航系统.分别对高度、姿态角和速度误差进行估计并进行算法仿真,结果表明:该方法具有较高精确度,对未来无...     

无人机编队飞行导航方法及其仿真研究

研究无人机导航优化编队问题,编队各成员的高精度定位是无人机协同编队飞行的关键技术和难点.为了提高编队定位的精度,提出了一种长机组合导航和长/僚机相对测量的编队成员导航定位方法,在编队导航系统模型的基础上,采用卡尔曼滤波原理估计出各成员的惯导误差,经校正后得到精确的导航信息,并进行仿真.仿真结果表明,改进的方法对所设计的长机与僚机的导航定位是完全可行的,编队各成员均获得较高的测地导航精度.     

五点约束最小二乘法估计光流速度场

提出一种二维光流场快速计算算法．首先求取当前像素点光流约束线与其8邻域像素点所对应8条光流约束线的交点；其次从8个交点中选取几何位置处于中间的4点，即速度处于中间值，且相互接近的4点，并以其对应的像素点与当前像素点一起构成5置信点；利用5置信点的光流约束方程构造一超定的方程组；最后利用最小二乘法求取当前像素点的光流速度．     

基于SLAM的旋翼无人机组合导航算法研究

在旋翼无人机组合导航中,针对缺乏GPS作为导航信号源的室内飞行环境,为了达到精确定位的目的,提出一种基于SLAM（simultaneous localization and mapping）的旋翼无人机组合导航算法。首先,引入双线性插值算法,实现基于扫描匹配的即时定位与地图构建;其次,对陀螺仪、加速度计和磁罗盘建立捷联惯导系统误差模型,针对旋翼无人机的使用环境对误差模型进行简化;最后,应用联邦卡尔曼滤波算法,设计组合导航系统模型,将SLAM算法和捷联惯导系统估计出的位置数据进行融合。仿真结果表明所设计基于SLAM的旋翼无人机组合导航算法能够进一步提高组合导航系统对旋翼无人机位姿估计的精度。     

实时图像匹配辅助无人机导航研究

针对GPS/INS组合导航系统在抗干扰性和精度上不能完全满足无人机导航系统要求的问题,利用实时图像匹配辅助无人机导航的方法进行仿真研究.首先,对输入图像进行目标识别定位,为了满足系统的实时性要求,研究采用光电混合联合变换相关器进行实时图像匹配,仿真结果表明,目标的识别定位精度小于两个像素,能够为无人机提供精确的导航信息;再根据摄影测量原理计算导航信息中的位置和姿态角信息;最后,将匹配定位得到的导航信息通过Kalman滤波器与惯导系统解算的导航信息进行信息融合,仿真结果表明,滤波后导航信息的误差均获得了较明显的收敛,提高了无人机导航系统的抗干扰性和导航精度.     

基于AFKF的多旋翼无人机组合导航技术研究

目前旋翼无人机组合导航系统大都使用扩展卡尔曼滤波算法,然而由于导航系统建模误差和传感器测量精度的影响,导航信息解算误差较大。为了改善旋翼无人机的飞行控制效果,应用自适应渐消卡尔曼滤波（Adaptive fading Kalman filter,AFKF）进行旋翼无人机组合导航解算,算法通过实时计算遗忘因子,对过去的数据权重进行削减,以提高扩展卡尔曼滤波算法的自适应能力。应用旋翼无人机真实飞行数据进行仿真,仿真结果表明,自适应渐消卡尔曼滤波算法能够有效抑制建模误差,弥补传感器测量精度不足,改善旋翼无人机组合导航解算结果。     

基于惯导/数据链的动态相对定位方法

针对卫星导航定位在复杂环境不可靠情况下如何实现无人机机间相对定位问题,提出一种基于机载惯性导航系统与机间数据链测距相结合的动态相对定位算法。该方法利用机载数据链通信测距能力与机载惯性导航系统输出的无人机速度矢量信息结合,建立机间相对定位模型,通过最小二乘法对无人机之间的相对位置进行估计,实现无人机机间的实时相对定位能力。由于通过最小二乘法解算出的相对定位结果依然存在误差,针对最小二乘法相对定位误差,提出秩亏网平差算法对无人机机群间的相对定位误差进行校正。仿真结果表明：基于最小二乘法的相对定位方法可以减缓惯性导航系统相对定位误差发散速度并且将惯导相对定位精度提高到3倍左右,通过秩亏网平差算法校正将最小二乘相对定位精度提高2倍。     

基于WIFI指纹的无人机室内定位策略

针对无人机室内定位问题,提出一种WIFI指纹定位与多传感器融合的定位方法.分析三维空间的WIFI指纹定位方法应用于无人机定位的难点,利用超声波传感器测量的无人机高度信息将定位匹配范围缩减至邻近的两个层面,提升WIFI定位的速度;设计卡尔曼滤波器,将WIFI定位结果作为卡尔曼滤波器预测阶段的输入,通过融合惯性传感器信息得到更准确的无人机位置估计,采用数据拟合的方法对定位结果进一步优化.仿真结果表明,该定位方法可实现无人机室内定位,有良好的定位速度和精度.     

基于粒子滤波的无人机自主轨迹视觉导航控制方法研究

针对现有无人机导航控制方法存在的控制效果不佳的问题,本文提出一种基于粒子滤波的无人机自主轨迹视觉导航控制方法研究。利用粒子滤波算法,实现对无人机自主轨迹视觉导航控制方法的优化设计。采用栅格法构建无人机飞行环境地图,根据无人机的机械组成结构和工作原理,构建运动状态模型。利用内置的摄像机设备采集视觉图像,执行图像灰度转换、几何校正、滤波等预处理步骤。通过对视觉图像的特征提取,判断当前环境是否存在障碍物。利用粒子滤波算法确定无人机位姿,结合障碍物识别结果规划无人机的自主飞行轨迹。将位置、速度和姿态角的控制量计算结果,输入到安装的导航控制器中,完成无人机的自主轨迹视觉导航控制任务。通过实测分析得出结论：应用设计的导航控制方法,其位置误差、速度误差以及姿态角误差均维持在预设值以下,即设计的导航控制方法具有良好的控制效果。     

基于视觉惯性里程计与语义信息的无人机SLAM方法研究

室内环境中存在丰富的语义信息,可以使机器人更好地理解环境,提高机器人位姿估计的准确性。虽然语义信息在机器人同时定位与地图构建(SLAM)领域得到了深入研究和广泛应用,但是在环境准确感知、语义特征提取和语义信息利用等方面还存在着很多困难。针对上述难点,提出了一种基于视觉惯性里程计算法与语义信息相结合的新方法,该方法通过视觉惯性里程计来估计机器人的状态,通过校正估计,构建从语义检测中提取的几何表面的稀疏语义地图;通过将检测到的语义对象的几何信息与先前映射的语义信息相关联来解决视觉惯性里程计和惯性测量单元的累积误差问题。在室内环境中对装备RGB-D深度视觉和激光雷达的无人机进行验证实验,结果表明,该方法比视觉惯性里程计算法取得了更好的结果。应用结合语义信息和视觉惯性里程计的SLAM算法表现出很好的鲁棒性和准确性,该方法能提高无人机导航精度,实现无人机智能自主导航。     

一套完整的基于视觉光流和激光扫描测距的室内无人机导航系统

提出了一套室内四旋翼无人机控制, 导航, 定位和地图构建的完整解决方案. 无人机机载系统包括三个主要传感器, 即惯性测量单元, 下视相机和激光扫描测距仪. 经过处理, 融合这些传感器的测量数据, 无人机能够可靠的估计自己的飞行速度和实时位置, 并且沿着室内的墙壁进行无碰撞飞行. 通过收集一个完整飞行实验的数据, 无人机的飞行路径和在室内的环境也可以被很好地估计出来. 这套系统中的自主导功能不需要任何远程传感信息或脱机计算能力. 这套室内导航方案的性能和可靠性已在实际的飞行实验中被验证.     

UAV navigation based on videosequences captured by the onboard video camera

We propose an approach to navigation for an unmanned aerial vehicle based on finding elements of motion (EM) (linear and angular velocities) by processing the field of local velocities for the motion of an image taken by an onboard video camera. The field of velocities for the image motion, the so-called optical flow (OF), is a linear function of the EM, which allows to use it to find the latter and thus provides an additional way of navigation, which can be rather efficiently used for certain specific problems solved by an UAV in autonomous flight. In this work, we use an algorithm for computing the OF for a given motion of the vehicle (direct problem) and show how to reconstruct the motion of the vehicle by OF observations with methods of statistical estimation (inverse problem).     

Development of a GPU Accelerated Terrain Referenced UAV Localization and Navigation Algorithm

This study focuses on localization and navigation of Unmanned Air Vehicles (UAVs) based on digital terrain map data. The solution to the Terrain Referenced Localization and Navigation (TERELONA) or Terrain Referenced Navigation (TRN) is described by using particle filter. In many UAV applications one of the most important points is to provide accurate location information continuously. TERELONA system can supply the air vehicle with the accurate position information with a bounded error. In this paper, the particle filtering method as an implementation of Bayesian approach to the terrain referenced localization and navigation is described. The radar altimeter measurements are used as an implicit representation of aircraft position. Whenever new measurements are taken from radar altimeter, they are compared to the Digital Terrain Map (DTM) data in order to fix a position. The solution is represented, in a Bayesian framework, by a set of particles with their corresponding weights. We have developed the terrain referenced localization and navigation algorithm based on the particle approximation. The proposed algorithm, which is developed in CUDA^TM, is also tested on the GPU environment using GPUmat software architecture. Thus, we can cope with the computational load of the very large initial horizontal position errors. The proposed algorithm has been implemented in MATLAB^TM environment and evaluated on simulated data. Simulations are conducted over an ASTER GDEM product which belongs to a region in northwest of Turkey. The simulation results are provided.     

Two-frame structure from motion using optical flow probability distributions for unmanned air vehicle obstacle avoidance

See-and-avoid behaviors are an essential part of autonomous navigation for Unmanned Air Vehicles (UAVs). To be fully autonomous, a UAV must be able to navigate complex urban and near-earth environments and detect and avoid imminent collisions. While there have been significant research efforts in robotic navigation and obstacle avoidance during the past few years, this previous work has not focused on applications that use small autonomous UAVs. Specific UAV requirements such as non-invasive sensing, light payload, low image quality, high processing speed, long range detection, and low power consumption, etc., must be met in order to fully use this new technology. This paper presents single camera collision detection and avoidance algorithm. Whereas most algorithms attempt to extract the 3D information from a single optical flow value at each feature point, we propose to calculate a set of likely optical flow values and their associated probabilities—an optical flow probability distribution. Using this probability distribution, a more robust method for calculating object distance is developed. This method is developed for use on a UAV to detect obstacles, but it can be used on any vehicle where obstacle detection is needed.     

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.