基于视觉的目标定位技术的研究进展

基于视觉的目标定位是目前计算机视觉领域的研究热点。就现阶段基于视觉的定位技术进行了综述,着重介绍了基于单目视觉定位、基于双目视觉定位和基于全景视觉定位3类定位技术的研究现状,并分析了各自的优缺点。最后就基于视觉定位方法的发展趋势做了简要分析,以期为以后基于视觉定位问题的研究提供参考。     

DSP的MAVLink微型无人机通信协议移植与应用

MAVLink微型无人机通信协议广泛应用于地面控制站与PIXHAWK、SLUGS和ArduPilot等无人机飞控平台的通信。MAVLink已被证明能够工作在ARM7、ATmega、STM32等微控制器上,并能在Windows、Linux和MacOS操作系统下开发。由于MAVLink与DSP不兼容,故本文提出一种将MAVLink移植到DSP,并通过WiFi与地面控制站软件QGroundControl进行通信的方法。实验证明,该方法能够使MAVLink有效兼容DSP,并且通过SPI转WiFi连接,减小了CPU开销,提高了数据传输速率,减少了数据的丢失情况。     

基于深度学习的多旋翼无人机单目视觉目标定位追踪方法

针对无人机对目标的识别定位与跟踪,本文提出了一种基于深度学习的多旋翼无人机单目视觉目标识别跟踪方法,解决了传统的基于双目摄像机成本过高以及在复杂环境下识别准确率较低的问题。该方法基于深度学习卷积神经网络的目标检测算法,使用该算法对目标进行模型训练,将训练好的模型加载到搭载ROS的机载电脑。机载电脑外接单目摄像机,单目摄像头检测目标后,自动检测出目标在图像中的位置,通过采用一种基于坐标求差的优化算法进行目标位置准确获取,然后将目标位置信息转化为控制无人机飞行的期望速度和高度发送给飞控板,飞控板接收到机载电脑发送的跟踪指令,实现对目标物体的跟踪。试验结果验证了该方法可以很好的进行目标识别并实现目标追踪     

On Constrained Nonlinear Tracking Control of a Small Fixed-wing UAV

The problem of constrained nonlinear tracking control for a small fixed-wing unmanned air vehicles (UAV) is considered. With the UAV equipped with low-level autopilots, the twelve-state model of the UAV is reduced to a six-state model with heading, air speed, and altitude command inputs. Three different approaches based on the state dependent Riccati equation (SDRE), Sontag’s formula, and aggressive selection from a satisficing control set are proposed to design the heading and air speed control commands. Those approaches are compared with each other graphically to show their strength and weakness under different scenarios. High-fidelity simulation results on a six-degree-of-freedom twelve-state fixed-wing UAV model are presented to demonstrate the performance of the three approaches.     

基于视觉的同时定位与地图构建的研究进展

基于视觉的同时定位与地图构建（VSLAM）是目前在机器人定位方面的热门研究课题,在机器人自身的定位以及场景识别、任务执行、路径规划等方面发挥着重要的作用。针对VSLAM的应用领域和发展趋势进行总结和归纳,分析了VSLAM的基本原理。在此基础上,从间接法和直接法两个方面对VSLAM关键技术和最新的研究进展进行了阐述,对比分析不同方法的优点和实现难点。最后展望了VSLAM的未来发展趋势和研究方向。     

Real-time Implementation and Flight Tests using Linear and Nonlinear Controllers for a Fixed-wing Miniature Aerial Vehicle (MAV)

International Journal of Control, Automation and Systems - This work presents the real-time implementation of four linear controllers and one nonlinear control technique (known as backstepping),...     

Two‐stage 3D model‐based UAV pose estimation: A comparison of methods for optimization

Particle Filters (PFs) have been successfully used in three‐dimensional (3D) model‐based pose estimation. Typically, these filters depend on the computation of importance weights that use similarity metrics as a proxy to approximate the likelihood function. In this paper, we explore the use of a two‐stage 3D model‐based approach based on a PF for single‐frame pose estimation. First, we use a classifier trained in a synthetic data set for Unmanned Aerial Vehicle (UAV) detection and a pretrained database indexed by bounding boxes properties to obtain an initial rough pose estimate. Second, we employ optimization algorithms to optimize the used similarity metrics and decrease the obtained error. We have tested four different algorithms: (a) Particle Filter Optimization (PFO), (b) Particle Swarm Optimization (PSO), (c) modified PSO, and (d) an approach based on the evolution strategies present in the genetic algorithms named Genetic Algorithm‐based Framework (GAbF). To check the quality of the estimate on each iteration, we have tested several similarity metrics (color, edge, and mask‐based) based on the UAV Computer‐Aided Design (CAD) model. The framework is applied to the outdoor pose estimation of a fixed‐wing UAV for autonomous landing in a Fast Patrol Boat (FPB). We extend our previous approach by adopting a better problem formulation, using Deep Neural Networks (DNNs) for UAV detection, making the comparison between the used similarity metrics, comparing pose optimization schemes, and showing new results. The future work will focus on the inclusion of this scheme in a tracking architecture to increase the accuracy of the result between observations.     

仿鸟扑翼飞行器姿态镇定研究

针对仿鸟扑翼飞行器的欠驱动特性,提出了一种简化其飞行控制问题并实现其局部渐近稳定的控制方法．建立并分析了仿鸟扑翼飞行器的动力学和运动学模型,证明其控制问题等价于升力、推力独立可控情况下的姿态控制问题．进一步分析的结果表明,仿鸟扑翼飞行器的升力、推力都是独立可控的,其姿态控制为耦合输入下刚体的姿态控制问题．通过设计光滑时变反馈控制律实现姿态控制的局部渐近稳定,从而解决扑翼飞行器的飞行控制问题．     

无人机多传感器的集成与同步控制

研发无人机多功能测量系统,对搭载平台的构建、多传感器同步与标定进行关键技术攻关,给出可行的解决方案。实验表明,该方案可有效应用于实际生产作业,极大提升作业质量和效率。     

微小型飞行器航向系统设计

从实用的角度出发,给出了微小型飞行器的航向系统(包括航向测量和航向控制)体系结构．设计了地磁航向、捷联航向、GPS航向等3种常用的航向测量手段在微小型系统中的实现方案,分析了其各自的优、缺点,提出了一种实用的、基于数据融合的航向获取方法．在航向测量的基础上,又设计了针对微小型飞行器的航向控制方案,并应用于某小型固定翼飞行器的飞行控制系统中．进行了自主飞行试验,取得了预期的试验效果．     

LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR CONTROL

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.     

基于机器视觉的高速公路车辆停车检测

为减少高速公路上车辆追尾等交通事故的发生,研究了基于机器视觉的车辆停车检测方法.检测系统采用当前帧和背景帧差分的方法实现了检测线圈范围内图像特征提取.分析了车辆从运动到静止的特征提取值图,并利用车辆存在阈值、车辆存在值、系统最小车辆存在值、系统不相似度阈值、系统相似度值、系统相似度最小值、车辆不存在值和系统最小车辆不存在值等参数实现了车辆停车检测.试验表明,该方法能够有效的将停靠在路面上的车辆检测出来.     

An Improved Evolvable Oscillator and Basis Function Set for Control of an Insect-Scale Flapping-Wing Micro Air Vehicle

This paper introduces an improved evolvable and adaptive hardware oscillator design capable of supporting adaptation intended to restore control precision in damaged or imperfectly manufactured insect-scale flapping-wing micro air vehicles. It will also present preliminary experimental results demonstrating that previously used basis function sets may have been too large and that significantly improved learning times may be achieved by judiciously culling the oscillator search space. The paper will conclude with a discussion of the application of this adaptive, evolvable oscillator to full vehicle control as well as the consideration of longer term goals and requirements.     

In‐water visual ship hull inspection using a hover‐capable underwater vehicle with stereo vision

Underwater visual inspection is an important task for checking the structural integrity and biofouling of the ship hull surface to improve the operational safety and efficiency of ships and floating vessels. This paper describes the development of an autonomous in‐water visual inspection system and its application to visual hull inspection of a full‐scale ship. The developed system includes a hardware vehicle platform and software algorithms for autonomous operation of the vehicle. The algorithms for vehicle autonomy consist of the guidance, navigation, and control algorithms for real‐time and onboard operation of the vehicle around the hull surface. The environmental perception of the developed system is mainly based on optical camera images, and various computer vision and optimization algorithms are used for vision‐based navigation and visual mapping. In particular, a stereo camera is installed on the underwater vehicle to estimate instantaneous surface normal vectors, which enables high‐precision navigation and robust visual mapping, not only on flat areas but also over moderately curved hull surface areas. The development process of the vehicle platform and the implemented algorithms are described. The results of the field experiment with a full‐scale ship in a real sea environment are presented to demonstrate the feasibility and practical performance of the developed system.     

基于MEMS技术的SU-8仿昆虫微扑翼飞行器设计及制作

为研制一种轻质仿昆虫微扑翼飞行器,提出了采用微机电系统(MEMS)领域的SU-8光刻胶作为结构材料的制作方案.基于仿生学原理和微机电系统加工技术,设计微扑翼飞行器结构及MEMS 工艺方法.研究结果表明,该种结构设计及制作方案满足设计要求,为仿昆虫微扑翼飞行器的研制提供了一种很好的途径.     

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。     

Precise pose estimation of the NASA Mars 2020 Perseverance rover through a stereo-vision-based approach

Visual Odometry (VO) is a fundamental technique to enhance the navigation capabilities of planetary exploration rovers. By processing the images acquired during the motion, VO methods provide estimates of the relative position and attitude between navigation steps with the detection and tracking of two-dimensional (2D) image keypoints. This method allows one to mitigate trajectory inconsistencies associated with slippage conditions resulting from dead-reckoning techniques. We present here an independent analysis of the high-resolution stereo images of the NASA Mars 2020 Perseverance rover to retrieve its accurate localization on sols 65, 66, 72, and 120. The stereo pairs are processed by using a 3D-to-3D stereo-VO approach that is based on consolidated techniques and accounts for the main nonlinear optical effects characterizing real cameras. The algorithm is first validated through the analysis of rectified stereo images acquired by the NASA Mars Exploration Rover Opportunity, and then applied to the determination of Perseverance's path. The results suggest that our reconstructed path is consistent with the telemetered trajectory, which was directly retrieved onboard the rover's system. The estimated pose is in full agreement with the archived rover's position and attitude after short navigation steps. Significant differences (~10–30 cm) between our reconstructed and telemetered trajectories are observed when Perseverance traveled distances larger than 1 m between the acquisition of stereo pairs.     

Autonomous Vehicles and Avoiding the Trolley (Dilemma): Vehicle Perception,Classification, and the Challenges of Framing Decision Ethics

Abstract

This article aims to introduce a degree of technological and ethical realism to the framing of autonomous vehicle perception and decisionality. The objective is to move the socioethical dialog surrounding autonomous vehicle decisionality from the dominance of “trolley framings” to more pressing ethical issues. The article argues that more realistic ethical framings of autonomous vehicle technologies should focus on the matters of HMI, machine perception, classification, and data privacy, which are some distance from the decisionality framing premise of the MIT Moral Machine experiment. To support this claim the article appeals to state-of-the-art technologies and emerging technologies concerning autonomous vehicle perception and decisionality, as a means to inform and frame ethical contexts. This is further supported by considering a context specific ethical framing for each time phase we anticipate regarding emerging autonomous vehicle technology.     

Real-Time Feature Matching in Image Sequences for Non-Structured Environments. Applications to Vehicle Guidance

This paper describes an integrated vehicle control system with visual feedback. A general-purpose, low-level feature matching method, able to work in real time without any strict assumptions on the environment structure or camera parameters, generates low-level matching results, which are used as source of data for applications like mobile object tracking, among others. A generalized predictive path-tracking control approach keeps the vehicle on the trajectory defined by the moving target. In the low-level matching process, block-based features (windows) are selected and tracked along a stream of monocular images; least residual square error and similarity between clusters of features are used as constraints to select the right matching pair between multiple candidates. Real-time performance is achieved through optimized algorithms and a parallel DSP-based multiprocessor system implementation. Object detection and tracking is motion-based, and does not require a predefined model of the target. The integrated control system has been tested on the ROMEO-3R experimental vehicle.