Design of a road vehicle detection system based on monocular vision

In order to decrease vehicle crashes, a new rear view vehicle detection system based on monocular vision is designed. First, a small and flexible hardware platform based on a DM642 digtal signal processor （DSP） micro-controller is built. Then, a two-step vehicle detection algorithm is proposed. In the first step, a fast vehicle edge and symmetry fusion algorithm is used and a low threshold is set so that all the possible vehicles have a nearly 100% detection rate （TP） and the non-vehicles have a high false detection rate （FP）, i. e., all the possible vehicles can be obtained. In the second step, a classifier using a probabilistic neural network （PNN） which is based on multiple scales and an orientation Gabor feature is trained to classify the possible vehicles and eliminate the false detected vehicles from the candidate vehicles generated in the first step. Experimental results demonstrate that the proposed system maintains a high detection rate and a low false detection rate under different road, weather and lighting conditions.

Mobile Robot Localization and Navigation System Based on Monocular Vision

A system for mobile robot localization and navigation was presented.With the proposed system,the robot can be located and navigated by a single landmark in a single image.And the navigation mode may be following-track,teaching and playback,or programming.The basic idea is that the system computes the differences between the expected and the recognized position at each time and then controls the robot in a direction to reduce those differences.To minimize the robot sensor equipment,only one omnidirectional camera was used.Experiments in disturbing environments show that the presented algorithm is robust and easy to implement,without camera rectification.The rootmean-square error(RMSE) of localization is 1.4,cm,and the navigation error in teaching and playback is within 10,cm.

Monocular Vision Based Boundary Avoidance for Non-Invasive Stray Control System for Cattle: A Conceptual Approach

Building fences to manage the cattle grazing can be very expensive;cost inefficient. These do not provide dynamic control over the area in which the cattle are grazing. Existing virtual fencing techniques for the control of herds of cattle, based on polygon coordinate definition of boundaries is limited in the area of land mass coverage and dynamism. This work seeks to develop a more robust and an improved monocular vision based boundary avoidance for non-invasive stray control system for cattle, with a view to increase land mass coverage in virtual fencing techniques and dynamism. The monocular vision based depth estimation will be modeled using concept of global Fourier Transform (FT) and local Wavelet Transform (WT) of image structure of scenes (boundaries). The magnitude of the global Fourier Transform gives the dominant orientations and textual patterns of the image;while the local Wavelet Transform gives the dominant spectral features of the image and their spatial distribution. Each scene picture or image is defined by features v, which contain the set of global (FT) and local (WT) statistics of the image. Scenes or boundaries distances are given by estimating the depth D by means of the image features v. Sound cues of intensity equivalent to the magnitude of the depth D are applied to the animal ears as stimuli. This brings about the desired control as animals tend to move away from uncomfortable sounds.

Calibration of laser beam direction based on monocular vision

In the laser displacement sensors measurement system,the laser beam direction is an important parameter.Particularly,the azimuth and pitch angles are the most important parameters to a laser beam.In this paper,based on monocular vision,a laser beam direction measurement method is proposed.First,place the charge coupled device(CCD)camera above the base plane,and adjust and fix the camera position so that the optical axis is nearly perpendicular to the base plane.The monocular vision localization model is established by using circular aperture calibration board.Then the laser beam generating device is placed and maintained on the base plane at fixed position.At the same time a special target block is placed on the base plane so that the laser beam can project to the special target and form a laser spot.The CCD camera placed above the base plane can acquire the laser spot and the image of the target block clearly,so the two-dimensional(2D)image coordinate of the centroid of the laser spot can be extracted by correlation algorithm.The target is moved at an equal distance along the laser beam direction,and the spots and target images of each moving under the current position are collected by the CCD camera.By using the relevant transformation formula and combining the intrinsic parameters of the target block,the2D coordinates of the gravity center of the spot are converted to the three-dimensional(3D)coordinate in the base plane.Because of the moving of the target,the3D coordinates of the gravity center of the laser spot at different positions are obtained,and these3D coordinates are synthesized into a space straight line to represent the laser beam to be measured.In the experiment,the target parameters are measured by high-precision instruments,and the calibration parameters of the camera are calibrated by a high-precision calibration board to establish the corresponding positioning model.The measurement accuracy is mainly guaranteed by the monocular vision positioning accuracy and the gravity center extraction accuracy.The experimental results show the maximum error of the angle between laser beams reaches to0.04°and the maximum error of beam pitch angle reaches to0.02°.

Research on Vehicle Anti-collision Technique Based on Monocular Vision

Vehicle anti-collision technique is a hot topic in the research area of Intelligent Transport System. The research on preceding vehicles detection and the distance measurement, which are the key techniques, makes great contributions to safe-driving. This paper presents a method which can be used to detect preceding vehicles and get the distance between own car and the car ahead. Firstly, an adaptive threshold method is used to get shadow feature, and a shadow!area merging approach is used to deal with the distortion of the shadow border. Region of interest(ROI) is obtained using shadow feature. Then in the ROI, symmetry feature is analyzed to verify whether there are vehicles and to locate the vehicles. Finally, using monocular vision distance measurement based on camera interior parameters and geometrical reasoning, we get the distance between own car and the preceding one. Experimental results show that the proposed method can detect the preceding vehicle effectively and get the distance between vehicles accurately.

基于声全息法和单目视觉技术的柴油机噪声可视化实现

柴油机属于多噪声耦合动力装置,其噪声的识别和控制是内燃机研究领域的难点。为实现柴油机噪声的可视化,本研究结合了近场声全息法与单目视觉技术,开发了基于Labview的声像匹配模块,测试系统人机界面友好,并通过已知声源试验验证了声像匹配模块的正确性。将验证后的测试系统应用于高压共轨柴油机对主/次推力侧声源进行识别,结果表明:在大气压力为80kPa、转速为1800r/min的最大扭矩工况下,主推力侧出现较大辐射噪声的位置为中冷器进气管、曲轴定时齿形带轮、排气管、脚架和涡轮增压器等;次推力侧辐射噪声峰值出现在起动机位置。

Accurate vision measurement for kinematic parameters of satellite separation tests

The accurate measurement of kinematic parameters in satellite separation tests has great significance in evaluating separation performance. A novel study is made on the measuring accuracy of monocular and binocular, which are the two main vision measurement methods used for kinematic parameters. As satellite separation process is transient and high-dynamic, it will bring more extraction errors to the binocular. Based on the design approach of intersection measure and variance ratio, the monocular method reflects higher precision, simpler structure and easier calibration for level satellite separation. In ground separation tests, a high-speed monocular system is developed to gain and analyze twelve kinematic parameters of a small satellite. Research shows that this monocular method can be widely applied for its high precision, with position accuracy of 0.5 mm, speed accuracy of 5 mm/s, and angular velocity accuracy of 1 (°)/s.

基于几何特征的铆接件精确装配视觉伺服控制方法

针对目前铆接件机器人装配多采用示教再现方式实现,主要应用于固定工况的问题,为满足铆钉数量多、工况多变的铆接件装配需求,提出一种基于几何特征的铆接件精确装配视觉伺服控制方法。首先,通过将相机安装在机器人末端执行器平台,搭建基于机器人的铆接件自主对准的单目视觉系统,完成相机、机器人末端与基坐标系标定;其次,选取铆钉平面初始图像中的4个铆钉圆心作为特征点,以特征点之间的连线构造直线特征,利用直线特征与相机旋转运动间的交互矩阵设计基于线特征的铆接件姿态控制律,使得铆钉与铆钉孔轴线平行,从而保证装配过程中铆接件的相互平行;再次,选取铆钉平面初始图像中的4个铆钉圆心作为特征点,1个铆钉的轮廓作为圆面积特征,利用点特征与相机旋转运动的交互矩阵设计铆接件的姿态补偿项,进而利用点特征和圆面积特征与相机平移运动间的交互矩阵设计与姿态控制律相解耦的位置控制律,实现铆钉与相应铆钉孔轴线的重合。最后,采用六自由度机器人进行铆接件自主对准装配仿真。结果表明,以铆接件内部铆钉固有的图像特征作为控制输入,所设计的控制器能够实现铆接件的自主高精度对准。方法对于工业生产中多点对准装配的工况具有广泛的应用价值。

单目视觉测量汽车最小转弯直径研究

针对目前汽车最小转弯直径测量方法存在测量误差较大、安装测量复杂等问题,设计一套基于单目视觉定位的汽车最小转弯直径测量系统,该套系统由图像采集、数据处理及无线通信三部分组成。首先通过单目视觉定位获取相机的实时轨迹坐标,使用空间圆拟合算法对轨迹坐标进行处理,得到相机的最优轨迹圆半径;然后通过定位精度测试和实车试验构建测量模型,最后计算出汽车转弯直径。结果表明,该测量系统测量精度高,能够满足厘米级的测量要求,可实现汽车最小转弯直径的有效测量。

基于正十二面体靶标多机器人定位系统研究

针对基于单目视觉的室内定位系统精度差的问题,提出了一种基于AprilTag标签和正多面体的正十二面体AprilTag靶标,并通过单目视觉检测该靶标的相对空间位姿。该靶标装置通过在正十二面体各个表面粘贴AprilTag标签,扩展了靶标的检测范围,同时结合多面体之间的约束关系,提升了位姿检测精度。通过搭建实验平台实施该靶标的位姿检测精度验证,实验结果表明提出的正十二面体AprilTag靶标的相对位姿检测精度明显优于单个AprilTag标签位姿检测精度,当采用相同尺寸的AprilTag标签时,相对位置误差降低了约83.6%,同时在3个轴向上的相对姿态误差分别减少了约95.2%、94.6%、89.1%。

微型仿生扑翼飞行器机载单目视觉系统的设计

近年来仿生扑翼飞行器利用视觉系统自主飞行成为一个具有广泛前景的研究方向,然而,其有限的带载能力对视觉传感器的类型、尺寸和重量提出了严格要求。目前商用图像处理模块的尺寸和重量较大,且需要回传图像信息至地面控制系统处理,文章旨在设计一款轻量化机载单目视觉系统,帮助微型仿生扑翼飞行器获取外界信息并实现智能自主的飞行。相比于其他图像处理模块,此系统以国产高算力芯片K210为核心进行设计,可脱离电脑端完成图像处理,尺寸仅为2.2 cm×2.3 cm,重量仅为3 g,内部兼容轻量化网络模型实现分类识别,通过串口进行信息交互,控制扑翼飞行器实现手势识别和目标追踪。

Verification of monocular and binocular pose estimation algorithms in vision-based UAVs autonomous aerial refueling system

With the high focus on autonomous aerial refueling(AAR), it becomes increasingly urgent to design efficient methods or algorithms for solving the AAR problems in complicated aerial environments. A vision-based technology for AAR is developed in this paper, and five monocular and binocular visual algorithms for pose estimation of the unmanned aerial vehicles(UAVs) are adopted and verified in this AAR system. The real-time on-board vision system is also designed for precise navigation in the UAVs docking phase. A series of out-door comparative experiments for different pose estimation algorithms are conducted to verify the feasibility and accuracy of the vision algorithms in AAR.

基于单目视觉的双机器人协同标定技术

由于机器人的基坐标系隐藏在底座内部,导致双机器人基坐标系间位姿关系的测量变得十分困难,而该关系作为机器人离线编程、协同控制的基石,对于多机器人系统的正常工作至关重要。为此提出了基于单目视觉的协同标定方法,利用单目视觉和棋盘格标定板求解双机器人基坐标系间位姿相对关系并使用激光跟踪仪标定法作为对照对双机器人基坐标系关系进行了标定,并通过双机器人位姿协同实验对标定的结果进行验证,最终得到基于单目视觉的协同标定方法所对应的双机器人位置协同误差最大值为1.278 mm,最小值为0.601 mm;姿态协同误差最大值则为0.481°。

基于单目视觉的工业机器人抓取系统设计

针对工业机器人在示教模式下上料无法对位姿出现偏差的工件完成抓取的问题,设计了一种基于单目视觉的工业机器人抓取系统。系统使用单目视觉采集工件图像,利用九点标定法将图像中的工件形心坐标转换为机器人坐标系下的坐标;通过图像处理算出工件的旋转角度,引导机器人运动至目标位置并配合抓手完成对工件的抓取。最后以PCB为对象搭建了抓取系统,经过多次抓取实验,发现该抓取系统位置误差在2 mm以内、姿态误差在1.5°以内,能够满足自适应抓取工件的定位要求。

基于无人机影像的输电线路自动巡检系统

为了有效确保输电线路安全性,增强无线通信功能,设计了基于无人机影像的输电线路自动巡检系统。搭建无人机巡检环境,采用无线通信模块实现无人机与地面站连接功能,利用导航模块调整无人机飞行路线和角度,使用巡检任务管理模块生成巡检任务,基于单目视觉算法获取无人机与输电线路之间安全距离。通过接收输电线路巡检影像,利用卷积神经网络模型识别输电线路巡检影像内存在的输电线路缺陷,引入ELU非线性激活函数实现输电线路自动巡检。实验结果表明,该系统具备较强的无线通信功能,且对焦耗时较短、运行稳定性较好,可有效确保输电线路安全性。

自动上样系统的上样点自动标定方法

随着自动上样机器人在蛋白质晶体衍射实验线站的普及,对机器人上样位置的坐标标定成为线站日常维护不可避免的工作。该方法通过颜色提取和边缘检测,分别提取激光传感器光斑和测角头图像特征,并通过中值滤波进行噪点消除。通过霍夫圆检测及最小闭圆拟合算法定位圆心像素坐标后,利用相机内、外参矩阵和手眼关系矩阵得到对应的基坐标系下坐标。最后,根据激光光斑与测角头的视觉定位坐标偏差,对机械手进行位置补偿,提高定位精度,实现上样点的自动标定。

Monocular vision for variable spray control system

The monocular vision-based system can obtain the leaf wall area characterizing the canopy parameter for online detection and real-time variable spraying,aiming to improve the accuracy of orchard spraying equipment and the utilization efficiency of pesticide.This study established a spraying system,in which canopy parameters were collected by monocular vision,and the spray volume decision coefficient was constructed by the leaf wall area and the L^(*)value in International Commission on Illumination Lab color space to control the duty cycle of each solenoid valve to achieve variable spraying.Four spray flow models were designed to determine the spray volume decision coefficient.The coefficients of determination of the spray volumes with the duty cycle range of 15%to 65%were all over 94 and the error of the leaf wall area values obtained using the improved super green algorithm(calculated as ExG=2.1G–1.1R–1.1B)was only 0.5%.The test showed that there is a negative relationship between canopy denseness and L^(*),and the value of L^(*)is smaller in the dense area compared with the sparse area;the actual flow generated by the system is similar to the theoretical flow when the duty cycle is 65%.The field validation tests showed that the variable spraying system could refine the droplet size and increase the droplet density to a certain extent with the same coverage rate,which had advantages over the continuous spraying.In terms of droplet deposition,DV0.1 and DV0.9 were reduced by 2μm and 18μm,respectively,and the increase of droplet density to 75 droplets/cm2.At the same time,the improvement of droplet distribution uniformity and droplet penetration by 16%and 3%,respectively.Compared with continuous spraying,variable spraying can achieve 55.64%savings.The study demonstrates the feasibility of monocular vision in guiding spraying operations and provides a reference for the use of monocular vision in plant protection operations.

基于视觉/惯导的掘进机实时位姿测量方法研究

针对煤矿井下掘进机实时位姿自动检测的难题,通过分析比较现有几种位姿测量方法的优缺点,提出了一种基于视觉/惯导的掘进机位姿组合测量方法。该方法通过激光捷联式惯导系统得到掘进机的姿态信息、单目视觉测量掘进机的位置信息,从而实现掘进机实时位姿的5自由度测量。依托大柳塔煤矿的快掘设备对该方法进行了实验验证。结果表明,姿态测量精度为0.1°,静态漂移的标准差不大于0.25°,位置定位精度优于1cm。该方法能够用于掘进机实时位姿测量,具有广阔的工程应用前景。

Monocular Vision-based Two-stage Iterative Algorithm for Relative Position and Attitude Estimation of Docking Spacecraft

Visual sensors are used to measure the relative state of the chaser spacecraft to the target spacecraft during close range ren- dezvous phases. This article proposes a two-stage iterative algorithm based on an inverse projection ray approach to address the relative position and attitude estimation by using feature points and monocular vision. It consists of two stages： absolute orienta- tion and depth recovery. In the first stage, Umeyama＇s algorithm is used to fit the three-dimensional （3D） model set and estimate the 3D point set while in the second stage, the depths of the observed feature points are estimated. This procedure is repeated until the result converges. Moreover, the effectiveness and convergence of the proposed algorithm are verified through theoreti- cal analysis and mathematical simulation.

多传感器测量系统的联合标定

将坐标测量机与单目视觉测量系统组合起来,提出了一种触发式测头与视觉测头联合标定的方法。选用内孔径为4mm的光面环规作为标定件,并利用转台、精密滑台配合视觉系统进行环规成像面的调整;然后,利用视觉测量系统提取环规内孔圆心图像坐标,包括设置图像的感兴趣区(ROI)、二值化、形态学去噪、亚像素边缘检测和利用最小二乘法提取圆心的图像坐标。通过拟合内孔圆柱面找到轴线,并利用接触传感系统提取相同位置圆心仪器坐标下坐标;最后,采用最小二乘法求解相关方程,完成联合标定。试验验证显示,该标定方法的标定精度高于6μm,优于已有的标定方法,能够满足测量系统联合测量的精度需要。