融合双目视觉与惯导信息的高效视觉里程计算法

为提高视觉里程计(VO)在大尺度环境下运行的实时性,提出一种融合双目视觉与惯导信息的视觉里程计算法,主要由前端位姿跟踪和后端局部地图优化两个线程组成.位姿跟踪线程首先使用惯导信息辅助光流法进行帧间特征点跟踪并估计相机初始位姿;接着通过最小化图像光度误差获取当前帧像素点与局部地图点的对应关系;而后最小化当前帧上局部地图点的重投影误差和惯性测量单元(IMU)预积分误差,得到当前帧准确的位姿估计.后端局部地图优化线程对滑动窗口内的关键帧提取特征点并三角化新地图点,使用光束平差法(BA)对逆深度参数化表示的地图点位置、关键帧位姿、速度以及陀螺仪和加速度计零偏进行滑窗优化,为前端提供更加精确的局部地图相机位姿和环境信息.在EuRoC数据集上的实验表明,相比于ORB-SLAM2、ORB-SLAM3算法,该融合双目视觉与惯导信息的视觉里程计算法的定位精度略有下降,但可以较大程度地提高位姿跟踪的实时性.     

基于双目视觉与IMU的组合导航算法

针对INS/GPS组合导航技术在GPS失效时难以有效导航的问题,提出一种基于误差状态粒子滤波器的双目视觉与IMU组合导航算法.通过把状态量映射到李代数上,在向量空间直接求解四元数随机粒子分布,使用粒子滤波器实现多传感器数据融合,完成载体运动信息的实时估计.使用EuRoC公开数据集对算法进行验证,实验结果表明,算法的定位...     

基于单目视觉的移动机器人导航算法研究进展

基于单目视觉的移动机器人导航的研究,涵盖了机器视觉、模式识别和多目标跟踪多个领域．其算法框架不仅成功应用于移动机器人导航,还为目标检测、识别与跟踪领域的研究提供了可供参考的模型．该综述将以算法发展历史为脉络,结合一些典型系统,通过对关键技术和算法结构的分析比较,总结算法本身的发展前景和由此发展起来的可供相关研究参考的算法框架．     

Efficient model-based tracking for robot vision

This paper proposes a real-time, robust and efficient three-dimensional (3D) model-based tracking algorithm. A virtual visual servoing approach is used for monocular 3D tracking. This method is similar to more classical non-linear pose computation techniques. A concise method for derivation of efficient distance-to-contour interaction matrices is described. An oriented edge detector is used in order to provide real-time tracking of points normal to the object contours. Robustness is obtained by integrating a M-estimator into the virtual visual control law via an iteratively re-weighted least-squares implementation. The method presented in this paper has been validated on several visual servoing experiments considering various objects. Results show the method to be robust to occlusion, changes in illumination and mis-tracking.     

基于虚拟导航线的农业机器人精确视觉导航方法

针对农田、野外环境中无人工标记情况下的导航问题,提出了一种基于虚拟导航线的农业机器人精确视觉导航方法。该方法不需要铺设导航线或者路标即可引导机器人行走直线。首先,根据需求确定需要跟踪的目标区域,之后控制机器人调整方向直到目标移至视野中央;其次,根据机器人和目标的位置确定参照目标,并依据两个目标的位置确定虚拟导航线;然后,动态更新导航线,并结合虚拟定标线和虚拟导航线确定偏移角度和偏移距离;最后,利用偏移参数构建模糊控制表,并以此实现对机器人旋转角度和行走速度的调整。实验结果表明,该算法能较为精确地实现对导航路线的识别,进而利用模糊控制策略使机器人沿直线向目标行走,且导航精度在10 cm以内。     

基于三目视觉系统的车辆导引方法

为使车辆在非结构化地形环境中实现自动导引,提出一种基于三目立体视觉系统的自适应地形分类方法。该地形分类方法利用三目视觉系统采集地形的几何信息与颜色信息, 方法中的几何分类器通过分析采集的数据对地形进行初步分类,而颜色分类器则在几何分类器的基础上对不同地形进行颜色标注。分类过程中,为使车辆能够有效地适应变化的地形环境,需根据分类所得新数据实时更新原有分类数据。该地形分类方法最终把可行驶的地面和不可行驶的任何地形作出分类并用不同颜色标注。从实验结果可看出,该方法可对实验中三目立体视觉系统所拍摄的地形作出准确分类。     

基于方向引导优化的视觉导航方法

为解决主动视觉导航在复杂路况条件下精度较差的问题,提出了一种基于方向引导优化的主动视觉导航参量计算方法。首先,建立了车辆物理坐标系与视觉图像坐标系的变换方程,通过Canny算子对视频图像进行初步的车道边缘线和道路标志线检测;接着,基于方向引导优化有效降低了阴影、积水等杂波对检测性能的干扰,并通过阈值优化的广义Hough变换实现车道线的内外边缘及道路标志线的精确检测;最后,基于检测结果计算出用于导航的中心引导线、偏离角度、距离等参量。实验结果表明,该方法能够有效且精确地获取主动导航系统的偏移参量。     

Painting robot with multi-fingered hands and stereo vision

In this paper, we describe a painting robot with multi-fingered hands and stereo vision. The goal of this study is for the robot to reproduce the whole procedure involved in human painting. A painting action is divided into three phases: obtaining a 3D model, composing a picture model, and painting by a robot. In this system, various feedback techniques including computer vision and force sensors are used. As experiments, an apple and a human silhouette are painted on a canvas using this system.     

基于立体视觉的高精度标定与测量方法

立体视觉测量系统中,光学系统产生的畸变使目标的成像偏离了理论成像点,导致系统产生测量误差。针对提高系统测量精度的问题,提出一种基于立体视觉的测量方法。首先,根据标定板上各角点的像素分辨率,拟合整个成像平面的四次多项式,且多项式的系数与物体到相机的距离成比例;然后,应用双目测距原理,测量被测物体的纵向距离;最后,基于所得的多项式,应用单目相机测量待测物体的横向尺寸。实验结果表明,对于所提方法,当物体距离相机5 m以内时,其纵向距离误差可以减小到5%以内;当物体距离相机1 m时,其横向宽度测量误差在0.5 mm内,逼近理论最高分辨率。     

一种新型的并联机器人位姿立体视觉检测系统

建立了一种并联机器人位姿立体视觉测量系统框架,主要包括图像采集与传输、摄像机标定、尺度不变量特征变换（SIFT）匹配、空间点重建和位姿测量五个部分。该系统基于SIFT,能够很好地处理图像在大视角有遮挡、平移、旋转、亮度和尺度变化时的特征点匹配,有较高的匹配精度,特别适用于对并联机器人多自由度和空间复杂运动的检测。最后使用该方法对并联机器人位姿检测做了仿真实验。     

基于单目视觉的移动机器人测距方法

本文针对多机器人编队过程中的跟踪控制,提出了一种跟踪机器人采用单目视觉技术获取前方被跟踪机器人距离信息的方法.该方法首先对跟踪机器人摄像机进行内参数标定,并在目标机器人背部设置视觉标记.然后系统获取目标机器人的含有视觉标记的单帧图像,预处理此图像,并识别出图像中的视觉标记所在的目标区域,用Hu氏不变矩计算该区域形心.最后推导出单目测距算法,利用图像信息和其它参数可以计算出两机器人之间的距离.实验结果表明,所设计的单目测距系统能得到准确的距离,为跟踪控制提供了重要的反馈信息.     

核磁共振图像归一化互相关非局部自适应去噪

为了更好地去除核磁共振(MR)图像中莱斯(Rician)分布的噪声,首先提出使用图像局部归一化互相关(NCC)作为几何结构相似性的一个表征,对传统非局部算法中使用灰度计算像素相似性权值的方法进行有效补充;然后,将改进方法分别应用于非局部均值算法和非局部最小线性均方误差估计算法,并根据局部信噪比(SNR)动态自适应地计算非局部算法中待滤波像素自身的加权值或者像素之间相似性阈值,达到对核磁图像自适应降噪的目的。实验结果表明,该算法可以更好地抑制核磁图像中的莱斯噪声,有效保留图像中细节信息,对核磁共振图像进一步的分析研究以及应用于临床诊断等具有非常重要的应用价值。     

基于粗精立体匹配的双目视觉目标定位方法

针对双目视觉系统定位精度较低的问题,提出一种基于粗-精立体匹配的双目视觉目标定位方法。该方法采用粗-精匹配策略：在粗匹配阶段使用基于Canny-Harris特征点的随机蕨算法对左右图中的目标进行识别,提取目标矩形区域的中心点,实现中心匹配;在精匹配阶段建立一种基于图像梯度信息的二值特征描述子,将中心匹配得到的右中心点作为估计值,设定像素搜索范围,于该区域中找出左中心点的最佳匹配点。最后,将得到的中心点匹配对代入平行双目视觉的数学模型中,实现目标定位。实验结果表明,在500 mm距离范围内,所提出定位方法的定位误差控制在7 mm内,平均相对定位误差为2.53%,相比其他方法具有定位精度高、运行时间短的优点。     

Fast normalized cross correlation for motion tracking using basis functions

Digital image-based elasto-tomography (DIET) is an emerging method for non-invasive breast cancer screening. Effective clinical application of the DIET system requires highly accurate motion tracking of the surface of an actuated breast with minimal computation. Normalized cross correlation (NCC) is the most robust correlation measure for determining similarity between points in two or more images providing an accurate foundation for motion tracking. However, even using fast Fourier transform (FFT) methods, it is too computationally intense for rapidly managing several large images. A significantly faster method of calculating the NCC is presented that uses rectangular approximations in place of randomly placed landmark points or the natural marks on the breast. These approximations serve as an optimal set of basis functions that are automatically detected, dramatically reducing computational requirements. To prove the concept, the method is shown to be 37-150 times faster than the FFT-based NCC with the same accuracy for simulated data, a visco-elastic breast phantom experiment and human skin. Clinically, this approach enables thousands of randomly placed points to be rapidly and accurately tracked providing high resolution for the DIET system.     

使用单目视觉的移动机器人导航方法

机器视觉与机器人的结合是未来机器人行业发展的一大趋势。在移动机器人的避障导航方案中,使用传统的传感器存在诸多问题,且获取的信息有限。提出一种基于单目视觉的移动机器人导航算法,在算法应用中,如果使用镜头焦距已知的相机,则无需对相机标定。为降低光照对障碍物边缘检测的影响,将机器人拍摄的彩色图像转换到HSI空间。采用canny算法对转换后的分量分别进行边缘检测,并合成检测结果。通过阈值处理过滤合成边缘,去除弱边缘信息,提高检测准确度。采用形态学处理连接杂散边缘,通过区域生长得到非障碍区域,并由几何关系建立图像坐标系与机器人坐标系之间的映射关系。利用结合隶属度函数的模糊逻辑得出机器人控制参数。实验结果表明,对图像颜色空间的转换降低了地面反光、阴影的影响,算法能有效排除地面条纹等的干扰并准确检测出障碍物边缘,而模糊逻辑决策方法提高了算法的鲁棒性和结果的可靠性。     

基于单目视频路径移动机器人导航方法研究

以单目视觉中双视图定位为基础,提出了一种基于单目视频路径的导航方法.算法基于单目摄像机提取一组有序的关键图像,以代表视频路径,使用Harris角点算法提取图像特征点,利用零均值归一化互相关算法进行图像匹配,根据单目摄像机双视图匹配点之间的约束关系求出基础矩阵并优化,得到位姿参数,完成了移动机器人定位,使其在视频路径的指导下实现自主导航.验证表明:方法在室外环境中具有较好的实用性.     

Mobile robot navigation and scene modeling using stereo fish-eye lens system

We present an autonomous mobile robot navigation system using stereo fish-eye lenses for navigation in an indoor structured environment and for generating a model of the imaged scene. The system estimates the three-dimensional (3D) position of significant features in the scene, and by estimating its relative position to the features, navigates through narrow passages and makes turns at corridor ends. Fish-eye lenses are used to provide a large field of view, which images objects close to the robot and helps in making smooth transitions in the direction of motion. Calibration is performed for the lens-camera setup and the distortion is corrected to obtain accurate quantitative measurements. A vision-based algorithm that uses the vanishing points of extracted segments from a scene in a few 3D orientations provides an accurate estimate of the robot orientation. This is used, in addition to 3D recovery via stereo correspondence, to maintain the robot motion in a purely translational path, as well as to remove the effects of any drifts from this path from each acquired image. Horizontal segments are used as a qualitative estimate of change in the motion direction and correspondence of vertical segment provides precise 3D information about objects close to the robot. Assuming detected linear edges in the scene as boundaries of planar surfaces, the 3D model of the scene is generated. The robot system is implemented and tested in a structured environment at our research center. Results from the robot navigation in real environments are presented and discussed. Received: 25 September 1996 / Accepted: 20 October 1996     

Efficient grid-based spatial representations for robot navigation in dynamic environments

In robotics, grid maps are often used for solving tasks like collision checking, path planning, and localization. Many approaches to these problems use Euclidean distance maps (DMs), generalized Voronoi diagrams (GVDs), or configuration space (c-space) maps. A key challenge for their application in dynamic environments is the efficient update after potential changes due to moving obstacles or when mapping a previously unknown area. To this end, this paper presents novel algorithms that perform incremental updates that only visit cells affected by changes. Furthermore, we propose incremental update algorithms for DMs and GVDs in the configuration space of non-circular robots. These approaches can be used to implement highly efficient collision checking and holonomic path planning for these platforms. Our c-space representations benefit from parallelization on multi-core CPUs and can also be integrated with other state-of-the-art path planners such as rapidly-exploring random trees.In various experiments using real-world data we show that our update strategies for DMs and GVDs require substantially less cell visits and computation time compared to previous approaches. Furthermore, we demonstrate that our GVD algorithm deals better with non-convex structures, such as indoor areas. All our algorithms consider actual Euclidean distances rather than grid steps and are easy to implement. An open source implementation is available online.     

On-line extraction of stable visual landmarks for a mobile robot with stereo vision

This paper proposes a method to extract on-line stable visual landmarks from sensory data obtained by stereo vision so as to adapt to changes of environment. Given a two-dimensional obstacle map, the robot first extracts vertical line segments which are distinct and inside planar surfaces not near boundary edges as they are expected to be observed reliably from various viewpoints. However, the extracted feature information such as position and length include uncertainty due to errors of vision and motion. The robot then reduces the uncertainty by matching the planar surface containing the features to the map. These processes are performed on-line in order to adapt to actual changes of lighting and the scene depending on the robot's view. Experimental results in real scenes show the validity of the proposed method.