基于近红外视觉的机器人室外定位系统

在光照条件可变且存在电磁干扰的环境下,针对机器人室外导航任务,提出了一种基于全景近红外视 觉和编码路标的自定位系统．通过近红外光源照明,利用全景视觉识别采用条形编码格式的路标,并利用扩展卡尔 曼滤波算法（EKF）融合视觉数据和里程计数据,从而实现机器人自定位．实验证明,该方法消除了室外大范围导航 时光照变化对机器人定位结果的影响．     

双曲面折反射全景图像的柱面解算研究

折反射全景视觉导航是自主移动机器人研究领域一项新兴技术,但由该视觉传感器生成的原始图像是严重畸变的,不适合机器人的目标定位和跟踪等任务。基于此,提出一种全景图像中心定位方法,将双线性内插值法应用于图像的采样,实现双曲面折反射全景图像的柱面还原解算。实验结果显示,还原后的图像人机交互性能有一定的提高。     

一个基于全景视觉的移动机器人导航系统的设计与实现

针对移动机器人路径规划与导航的实际应用,设计了一个基于全景视觉的移动机器人路径规划导航系统．首先,对导航系统的体系结构和功能进行描述．然后,分别就如何采用全景视觉传感器进行环境探索与地图创建,基于回归神经网络的广度优先搜索法和Voronoi骨架图法两种路径规划算法原理,以及如何实现按规划路径实施导航这三个方面进行了详细阐述．最后,结合实际机器人进行导航实验,评估导航系统的性能和路径规划算法的有效性．     

全景视觉在机器人自主定位中的应用

研究一种基于全景视觉的机器人自主定位的方法。利用光学视觉原理,设计了一种全景视觉传感器,从而获得机器人周围环境的全方位景物的图像。通过去除全景图像的噪声、分割颜色阈值、计算中心点等处理,识别出机器人周围景物的已知路标,采用三角定位法,计算出机器人的坐标,从而为机器人的导航、避碰等任务奠定良好的基础。实验结果表明,此方法对于机器人自主定位具有一定的可行性。     

基于全景近红外视觉的室外移动机器人定位系统

针对室外移动机器人定位、导航问题,提出了一种基于全景近红外视觉的路标定位系统。系统通过近红外主动照明降低了光照变化、阴影等因素的影响,利用全景摄像机获得大范围的路标定向信息。图像处理中改进大津法和路标跟踪的应用使识别路标更准确、更快速,三角定位算法确保能精确的计算出机器人的世界坐标。室外环境下移动机器人的定位实验结果表明,本系统具有较高的定位精度和良好的鲁棒性。     

全景视觉系统发展与应用

全景视觉系统具有视场范围大的特点,并且系统结构种类繁多,各有特色,已广泛应用在机器人导航、空间探测、视频监控、虚拟现实、环境感知技术等民用或军用领域;全景视觉系统由全景视觉光学成像系统、图像传感器、图像处理系统和输出图像显示等组成;文章主要从全景视觉成像角度对全景视觉系统进行分类,并详细介绍了每一类的全景成像原理、发展现状和应用情况,最后对每种类型全景视觉系统的优缺点给予分析总结。     

基于全景视觉的移动机器人地图创建与定位

提出一种高效的基于全景视觉的室内移动机器人地图构建和定位方法. 该方法充分利用全景视觉系统视野广阔、获取环境信息完整的特点, 根据全景图像生成环境描述子; 利用上述环境描述子描述环境, 创建拓扑地图, 将地图表示为环境描述子的集合. 在此基础上, 提出一种基于贝叶斯理论的定位方法, 根据当前全景摄像头的观测值, 利用已生成的地图完成状态跟踪, 全局定位和“绑架”定位. 最后通过实验验证了该方法的有效性, 并给出了计算成本分析.     

基于全景视觉的移动机器人同步定位与地图创建研究

提出了一种基于全景视觉的移动机器人同步定位与地图创建（Omni-vSLAM）方法．该方法提取 颜色区域作为视觉路标;在分析全景视觉成像原理和定位不确定性的基础上建立起系统的观测模型,定位出 路标位置,进而通过扩展卡尔曼滤波算法（EKF）同步更新机器人位置和地图信息．实验结果证明了该方法在 建立环境地图的同时可以有效地修正由里程计造成的累积定位误差．     

基于EKF的全景视觉机器人SLAM算法

研究全景视觉机器人同时定位和地图创建（SLAM）问题。针对普通视觉视野狭窄, 对路标的连续跟踪和定位能力差的问题, 提出了一种基于改进的扩展卡尔曼滤波（EKF）算法的全景视觉机器人SLAM方法, 用全景视觉得到机器人周围的环境信息, 然后从这些信息中提取出环境特征, 定位出路标位置, 进而通过EKF算法同步更新机器人位姿和地图库。仿真实验和实体机器人实验结果验证了该算法的准确性和有效性, 且全景视觉比普通视觉定位精度更高。     

基于机器视觉的矿用悬臂掘进机自主定位关键技术的研究

针对传统的悬臂式掘进机极少带定位,主要依靠人工放线来确定方位和角度,且目前井下导航技术受恶劣工况影响,存在精度不高、定位不准确等问题,本文采用机器视觉技术提出了一种悬臂式掘进机自动定位与导航技术的理念,促进井下推进设备的自动定位和轨迹收集。本文主要介绍了悬臂掘进机自动定位与导航技术的原理,对硬件设施进行了理论求证,以及加设配套定位统与程序的理念描述。按照机器视觉技术的应用,预计定位精度可达厘米级,总定位偏差非常小,应用效果较为可观,以期为后期实现煤矿井下设备的智能控制和无人化管理提供参考。     

一种基于视觉的移动机器人定位系统

具有自主的全局定位能力是自主式稳定机器人传感器系统的一项重要功能,为了实现这个目的,国内外均在不断地研究发展各种定位传感器系统,这里介绍了一种采用光学蝗全方位位置传感器系统,该传感器系统由主动式路标、视觉传感器、图象采集与数据处理系统组成,其视觉传感器和数据处理系统可安装在移动机器人上,然后可通过观测路标物「视角定位的方法,计算出机器人在世界坐标系中的位置和方向,实验证明,该系统可以只的在线定位,     

Localization methods for a mobile robot in urban environments

This paper addresses the problems of building a functional mobile robot for urban site navigation and modeling with focus on keeping track of the robot location. We have developed a localization system that employs two methods. The first method uses odometry, a compass and tilt sensor, and a global positioning sensor. An extended Kalman filter integrates the sensor data and keeps track of the uncertainty associated with it. The second method is based on camera pose estimation. It is used when the uncertainty from the first method becomes very large. The pose estimation is done by matching linear features in the image with a simple and compact environmental model. We have demonstrated the functionality of the robot and the localization methods with real-world experiments.     

Trends in the development of sensor systems and their use in some technological areas

The paper describes the trends in the development of sensor systems in the area of robotics. Presented are contact sensors with special emphasis on “skin” sensors for a robot's hand on the basis of piezo-electric materials, such as polyvinylidene fluoride (PVF₂). Among the methods of non-contact sensor perception, vision systems are considered to be most appropriate for robots. Described are industrial vision systems, image processing operations and image recognition. Reference is also made to proximity sensors. The paper further describes robot control systems utilizing sensory information and utilization of artificial intelligence theory in robotics.     

非接触式机器人直线轨迹测量系统

视觉的、造价低廉实用的非接触式机器人直线轨迹测量系统．该测量系统由结构光视觉传感 器、测量轨道、主控计算机及相关软件组成．视觉传感器可固定于机器人末端．当机器人末 端带动视觉传感器沿测量轨道做直线运动时，通过传感器测量相对测量轨道的连续位姿关系 ，就可间接描述机器人末端的运动轨迹．当重复同一直线运动时，可检测出机器人的轨迹重 复性．本文综述了机器人直线轨迹测量设备的研究现状，介绍了轨迹检测系统的测量原 理，重点讨论了该测量系统的两个关键技术：空间特征点的图象提取技术和三维坐标计算方 法，并描述了该系统的结构、性能指标和测量试验结果．     

Skeleton pruning as trade-off between skeleton simplicity and reconstruction error

The position and orientation of moving platform mainly depends on global positioning system and inertial navigation system in the field of low-altitude surveying, mapping and remote sensing and land-based mobile mapping system. However, GPS signal is unavailable in the application of deep space exploration and indoor robot control. In such circumstances, image-based methods are very important for self-position and orientation of moving platform. Therefore, this paper firstly introduces state of the art development of the image-based self-position and orientation method (ISPOM) for moving platform from the following aspects: 1) A comparison among major image-based methods (i.e., visual odometry, structure from motion, simultaneous localization and mapping) for position and orientation; 2) types of moving platform; 3) integration schemes of image sensor with other sensors; 4) calculation methodology and quantity of image sensors. Then, the paper proposes a new scheme of ISPOM for mobile robot — depending merely on image sensors. It takes the advantages of both monocular vision and stereo vision, and estimates the relative position and orientation of moving platform with high precision and high frequency. In a word, ISPOM will gradually speed from research to application, as well as play a vital role in deep space exploration and indoor robot control.     

Virtual sensors for human concepts—Building detection by an outdoor mobile robot

In human–robot communication it is often important to relate robot sensor readings to concepts used by humans. We suggest the use of a virtual sensor (one or several physical sensors with a dedicated signal processing unit for the recognition of real world concepts) and a method with which the virtual sensor can learn from a set of generic features. The virtual sensor robustly establishes the link between sensor data and a particular human concept. In this work, we present a virtual sensor for building detection that uses vision and machine learning to classify the image content in a particular direction as representing buildings or non-buildings. The virtual sensor is trained on a diverse set of image data, using features extracted from grey level images. The features are based on edge orientation, the configurations of these edges, and on grey level clustering. To combine these features, the AdaBoost algorithm is applied. Our experiments with an outdoor mobile robot show that the method is able to separate buildings from nature with a high classification rate, and to extrapolate well to images collected under different conditions. Finally, the virtual sensor is applied on the mobile robot, combining its classifications of sub-images from a panoramic view with spatial information (in the form of location and orientation of the robot) in order to communicate the likely locations of buildings to a remote human operator.     

Visual station keeping for floating robots in unstructured environments

This paper describes the use of vision for navigation of mobile robots floating in 3D space. The problem addressed is that of automatic station keeping relative to some naturally textured environmental region. Due to the motion disturbances in the environment (currents), these tasks are important to keep the vehicle stabilized relative to an external reference frame. Assuming short range regions in the environment, vision can be used for local navigation, so that no global positioning methods are required. A planar environmental region is selected as a visual landmark and tracked throughout a monocular video sequence. For a camera moving in 3D space, the observed deformations of the tracked image region are according to planar projective transformations and reveal information about the robot relative position and orientation w.r.t. the landmark. This information is then used in a visual feedback loop so as to realize station keeping. Both the tracking system and the control design are discussed. Two robotic platforms are used for experimental validation, namely an indoor aerial blimp and a remote operated underwater vehicle. Results obtained from these experiments are described.     

基于DSP的视觉传感器的开发及其在焊缝自动跟踪机器人上的应用

近年来视觉传感器在工业自动化和机器人导航领域得到越来越多的应用。本文提出了一种基于DSP微处理器的视觉传感器的设计与实现。视觉传感器采集环境图像并在DSP内核处理器执行图像处理算法，得到决策结果后直接输出给控制系统执行，从而避免了传输大量图像数据所需要的高带宽通讯通道的使用。开发的视觉传感器具有体积小、实时性能好、可扩展性强等特点，并且提供了常用的图像处理软件支持包。文中对系统的软件和硬件开发进行了详细阐述，最后在焊缝自动跟踪平台上的应用验证了传感器的实际整体性能可满足实际应用的需要。关于视觉传感器的下一步工作在最后进行了讨论。     

自主移动机器人智能导航研究进展

本文对当前在自主移动机器人智能导航研究中已被采用并取得的研究方法进行了１，并根据已取得的成果预测了移动机器人智能导航研究的发展趋势，指出视觉导航和传感器融合将是移动机器人智能导航的主要发展方向。