全景监控中图像校正技术的应用

鱼眼镜头是构建全景视觉最简单有效的方法之一,它的突出特点是一次性摄入185°视角内所有的信息,无盲区,无须考虑图像拼合和嵌接等问题.但鱼眼图像会有非常严重的畸变.该文在标定鱼眼图像光学中心和畸变系数的基础上应用球面等距投影模型实现超大广角畸变图像的校正与展开.该方法在群组视频会议、大范围监控系统、智能交通系统、机器人视觉导航中都具有广泛的推广应用价值.实验表明,该方法能得到比较满意的校正展开结果.     

一种改进的鱼眼畸变校正算法

畸变是鱼眼镜头的最大的问题,针对这一情况,提出一种利用双椭圆模型对鱼眼镜头进行畸变校正的算法,在改善鱼眼畸变的情况下,同时能够保障实时输出;对鱼眼图像进行边缘扫描和检测,采用线性拟合的方法获取鱼眼图像的光心和半径,经过双椭圆模型寻找校正前和校正后图像的映射关系,调用GPU加速处理,达到实时输出的效果,经过实验对比,针对鱼眼镜头引起的畸变问题进行校正并且能够实时输出;     

室内移动机器人新型视觉导航系统设计

视觉导航作为一种重要的机器人导航方式正日益受到人们的关注,为了解决目前采用视觉导航存在的算法设计复杂、导航性能差等问题,提出一种新的室内移动机器人视觉导航系统设计方案。首先阐述了该导航新方法的设计思想,然后介绍了采用该方法的导航系统的整体结构设计。利用放置在室内固定位置的摄像机采集室内环境的视频图像,设计了实时图像分析算法实现障碍物躲避、机器人位置跟踪等功能,为机器人的行走提供导航依据,同时通过无线通信的方式发送控制指令,实现对室内移动机器人的行走过程导航控制。     

一种鱼眼镜头成像立体视觉系统的标定方法

鱼眼镜头成像立体视觉系统在微小型机器人视觉导航和近距离大视场物体识别与定位中有着广泛的应用 .尽管鱼眼镜头摄像机具有很大的视场角 (接近 180°) ,但同时也引入严重的图像变形 ,常规的摄像机标定方法无法使用 .该文提出一种标定鱼眼镜头摄像机立体视觉系统的方法 .在鱼眼镜头变形模型的基础上 ,通过考虑鱼眼镜头成像的径向变形、偏心变形和薄棱镜变形 ,建立了鱼眼镜头成像的精确成像模型 ;然后 ,利用非线性迭代算法 ,精确求解摄像机外部参数、内部参数 .实验表明 ,使用该方法得到的立体视觉系统参数满足精确恢复大场景稠密深度图的要求 .     

基于CPLD的彩色视觉移动机器人路径跟踪系统

路径跟踪是机器人视觉导航控制基本技术之一,为使机器人沿地面彩色引导线自主运动,并能在适时离线执行任务后自动返航,提出了一种用可编程逻辑器件(CPLD)实现的视觉伺服PID控制方法.该方法利用图像特征反馈对其所跟踪的路经进行实时识别跟踪.仿真结果表明,该方法改善了控制算法的实时性,提高了移动机器人的路径跟踪精度与速度.     

基于双线性插值的鱼眼图像校正方法

鱼眼镜头具有大视场、短焦距等优点,近年来被广泛应用到不同的领域。由于鱼眼镜头成像存在较大的畸变,目前主要用来目标监测,在目标物体的识别方面应用得很少。为此,提出一种基于球面透视投影约束的鱼眼图像校正方法,并用双线性插值法对校正后的图像进行填充,为鱼眼镜头在目标物体识别跟踪方面的应用做了准备工作。实验结果表明采用上述方法能够很好地对鱼眼图像畸变进行校正,且校正后的鱼眼图像符合人的直观感觉,真实感较强,图像边缘清晰。     

一种基于斜率的摄像机畸变校正方法

普通 CCD摄像机在成像时都存在畸变成像误差 ,在机器人视觉检测及自动装配中 ,有效地进行误差校正对准确确定物体的位置具有重要的意义 .本文采用带有一阶径向畸变的小孔摄像机模型 ,提出一种基于线段斜率的方法 ,对摄像机镜头的径向畸变进行校正 ,不必标定太多的摄像机的外参数 ,方法简洁 ,适合于视觉系统中对摄像机畸变的实时校正 ,或对摄像机捕获的图像进行几何校正 .实验表明 ,具有很强的鲁棒性和较高的校正精度     

基于几何模型的鱼眼图像校正

在虚拟现实,机器人导航及视觉监控等领域,需要使用具有较大视角的鱼眼摄像机,这样得到的图像被称为鱼眼图像。鱼眼图像存在严重的变形和失真。本研究利用平面,柱面和球面经纬网格3种几何模型分别进行鱼眼图像校正,实现了真实场景的还原,并对校正结果进行分析和比较。     

嵌入式全景视觉实时目标识别

在嵌入式系统中,使用鱼眼镜头实现全景视觉,完成实时目标跟踪工作.采用FPGA+DSP+PC的硬件系统架构作为识别器,使用SAA7113H芯片采集图像,再由FPGA将采集后的图像存入到SRAM,因为使用鱼眼镜头采集后的图像是畸变的,所以采用FPGA对图像进行矫正,运用Freeman链码进行目标识别,尤其是对直线的识别,这些工作都需要大量的计算,高效的FPGA和Altera公司提供的IP核,加快目标识别速度,以达到目标跟踪的目的.     

基于柱面模型的鱼眼影像校正方法的研究

鱼眼镜头突破普通相机对视场的限制,然而它存在畸变。从基于球面透视的柱面模型出发,介绍如何对整幅鱼眼影像进行畸变校正,提出一种基于球面透视的柱面模型的等弧长映射方法,并给出具体算法的推导,实现了对鱼眼影像畸变的校正,取得了较好的效果。     

Visual Navigation in Natural Environments: From Range and Color Data to a Landmark-Based Model

This paper concerns the exploration of a natural environment by a mobile robot equipped with both a video color camera and a stereo-vision system. We focus on the interest of such a multi-sensory system to deal with the navigation of a robot in an a priori unknown environment, including (1) the incremental construction of a landmark-based model, and the use of these landmarks for (2) the 3-D localization of the mobile robot and for (3) a sensor-based navigation mode.For robot localization, a slow process and a fast one are simultaneously executed during the robot motions. In the modeling process (currently 0.1 Hz), the global landmark-based model is incrementally built and the robot situation can be estimated from discriminant landmarks selected amongst the detected objects in the range data. In the tracking process (currently 4 Hz), selected landmarks are tracked in the visual data; the tracking results are used to simplify the matching between landmarks in the modeling process.Finally, a sensor-based visual navigation mode, based on the same landmark selection and tracking, is also presented; in order to navigate during a long robot motion, different landmarks (targets) can be selected as a sequence of sub-goals that the robot must successively reach.     

Effective landmark placement for accurate and reliable mobile robot navigation

Being able to navigate accurately is one of the fundamental capabilities of a mobile robot to effectively execute a variety of tasks including docking, transportation, and manipulation. As real-world environments often contain changing or ambiguous areas, existing features can be insufficient for mobile robots to establish a robust navigation behavior. A popular approach to overcome this problem and to achieve accurate localization is to use artificial landmarks. In this paper, we consider the problem of optimally placing such artificial landmarks for mobile robots that repeatedly have to carry out certain navigation tasks. Our method aims at finding the minimum number of landmarks for which a bound on the maximum deviation of the robot from its desired trajectory can be guaranteed with high confidence. The proposed approach incrementally places landmarks utilizing linearized versions of the system dynamics of the robot, thus allowing for an efficient computation of the deviation guarantee. We evaluate our approach in extensive experiments carried out both in simulations and with real robots. The experiments demonstrate that our method outperforms other approaches and is suitable for long-term operation of mobile robots.     

Intelligent control for the vision-based indoor navigation of an A-life mobile robot

Realizing steady and reliable navigation is a prerequisite for a mobile robot, but this facility is often weakened by an unavoidable slip or some irreparable drift errors of sensors in long-distance navigation. Although perceptual landmarks were solutions to such problems, it is impossible not to miss landmarks occasionally at some specific spots when the robot moves at different speeds, especially at higher speeds. If the landmarks are put at random intervals, or if the illumination conditions are not good, the landmarks will be easier to miss. In order to detect and extract artificial landmarks robustly under multiple illumination conditions, some low-level but robust image processing techniques were implemented. The moving speed and self-location were controlled by the visual servo control method. In cases where a robot suddenly misses some specific landmarks when it is moving, it will find them again in a short time based on its intelligence and the inertia of the previous search motion. These methods were verified by the reliable vision-based indoor navigation of an A-life mobile robot.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

Vision Based Intelligent Wheel Chair Control: The Role of Vision and Inertial Sensing in Topological Navigation

This paper describes ongoing research on vision based mobile robot navigation for wheel chairs. After a guided tour through a natural environment while taking images at regular time intervals, natural landmarks are extracted to automatically build a topological map. Later on this map can be used for place recognition and navigation. We use visual servoing on the landmarks to steer the robot. In this paper, we investigate ways to improve the performance by incorporating inertial sensors. © 2004 Wiley Periodicals, Inc.     

Localization along routes,based upon iconic and Global Positioning System information in large-scale outdoor environments

This paper proposes a method of robot navigation in outdoor environments based upon panoramic view and Global Positioning System (GPS) information. Our system is equipped with a GPS navigator and a camera. The route scene can be described by three-dimensional objects extracted as landmarks from panoramic representations. For an environment having limited routes, a two-dimensional map can be made based upon routes scenes, assuming that the topological relation of routes at intersections is known. By using GPS information, the global position of a mobile robot can be known, and a coarse-to-fine method is used to generate an outdoor environment map and locate a mobile robot. First, a robot finds its approximate position based on the GPS information. Then, it identifies its location from the image information. Experimental results in outdoor environments are given.     

Qualitative spatial representations from task-oriented perception and exploratory behaviors

We describe motor and perceptual behaviors that have proven useful for indoor navigation of an autonomous mobile robot. These behaviors take advantage of the large amount of structure that characterizes many indoor, office-like environments. Based on pre-existing structural landmarks, a mobile robot has the ability to explore, map, and navigate one among several office buildings sharing similar structural features, while coping with slow environment variations and local dynamics. The mobile robot develops and maintains an internal spatial representation of the environment in terms of a topological and qualitative map. The types of structural features suitable as navigation landmarks largely depend upon the available sensors. Adequate navigation performance is achieved by subdividing perception and navigation into a number of behaviors layered upon a multi-threaded real-time control architecture.     

Learning to select distinctive landmarks for mobile robot navigation

In landmark-based navigation systems for mobile robots, sensory perceptions (e.g., laser or sonar scans) are used to identify the robot’s current location or to construct internal representations, maps, of the robot’s environment. Being based on an external frame of reference (which is not subject to incorrigible drift errors such as those occurring in odometry-based systems), landmark-based robot navigation systems are now widely used in mobile robot applications.The problem that has attracted most attention to date in landmark-based navigation research is the question of how to deal with perceptual aliasing, i.e., perceptual ambiguities. In contrast, what constitutes a good landmark, or how to select landmarks for mapping, is still an open research topic. The usual method of landmark selection is to map perceptions at regular intervals, which has the drawback of being inefficient and possibly missing ‘good’ landmarks that lie between sampling points.In this paper, we present an automatic landmark selection algorithm that allows a mobile robot to select conspicuous landmarks from a continuous stream of sensory perceptions, without any pre-installed knowledge or human intervention during the selection process. This algorithm can be used to make mapping mechanisms more efficient and reliable. Experimental results obtained with two different mobile robots in a range of environments are presented and analysed.     

Landmark detection in mobile robotics using fuzzy temporal rules

Detection of landmarks is essential in mobile robotics for navigation tasks like building topological maps or robot localization. Doors are one of the most common landmarks since they show the topological structure of indoor environments. In this paper, the novel paradigm of fuzzy temporal rules is used for detecting doors from the information of ultrasound sensors. This paradigm can be used both to model the necessary knowledge for detection and to consider the temporal variation of several sensor signals. Experimental results using a Nomad 200 mobile robot in a real environment produce 91% of doors were correctly detected, which show the reliability and robustness of the system.     

基于模糊控制信息融合方法的机器人导航系统

本文提出了一种基于模糊控制和信息融合的自主式移动机器人导航系统的实现方法。采用表格查询的模糊控制方法,对导航系统中的多传感器信息进行融合,实现了机器人的路径跟踪和自动纠偏的功能。该系统具有实现简单、响应快、鲁棒性好等特点,仿真实验和实际运行结果表明了该系统在具有引导线的环境中能有效实现自主式移动机器人的导航。