基于HALCON的机器人手眼标定精度分析与反演方法

通过视觉引导机器人完成抓取任务,机器人手眼标定的精度直接影响了抓取任务作业精度和抓取成功率。对于基于位置的机器人视觉引导系统,手眼标定的任务则是确定机器人坐标系与相机坐标系之间的位姿关系。通过HALCON平台,使用线性标定法实现了6DOF机器人的手眼标定。对手眼标定的结果进行反演,直观地展示了手眼标定的精确程度。最后通过采集多组不同数量的图片,在HALCON平台下验证了不同摄像机模型对手眼标定的精度影响,以及同种摄像机模型在不同数量图片的情况下手眼标定的标定精度。实验证明,根据位姿矩阵中待求解的未知量个数采集合适数量的图片和使用更精确的摄像机模型能够提高手眼标定的精度。     

基于眼固定安装方式的机器人定位系统

研发了基于眼固定安装方式的机器人定位系统,提出了一种方便有效的手眼标定方法。通过最小二乘法求解手眼坐标的变换关系,再根据工作台平面与摄像机成像模型的约束关系,求解出目标物体的三维位姿,并最终实现了机械手的精确定位。     

基于手眼立体视觉的机器人定位系统

研发了基于手眼的机器人定位系统,采用了眼在手上的单目摄像机,通过机械手的一次移动实现了立体视觉的功能.提出了一种方便有效手眼标定方法,避免了复杂的传统手眼标定过程,无需求解摄像机外参数和手眼变换矩阵.仅获取标定时刻的摄像机综合参数和机器人位姿,就可以在机器人基坐标系中视场范围内的任意两点进行检测,根据立体视觉的约束关系求解出目标物体在机器人基坐标中的位置,进而实现对目标物体的精确定位.     

一种新的机器人手眼关系标定方法

通过控制装有摄像机的机械手的运动,给出了一种新的机器人手眼系统标定方法．与以往算法的不同之处在于,在计算手眼关系的平移向量时,对摄像机坐标系进行虚设旋转变换使旋转转化为平移问题．该方法需要机械手平台做两次平移运动和一次旋转运动,只需要场景中两个特征点,所以具有方便性和实用性．同时,也给出了基于主动视觉的空间点深度值计算方法．     

基于视觉伺服的工业机器人位姿跟踪方法

目前，工业机器人工作大多是基于先精确示教后运行的，工作效率较低，因此采用视觉提高机器人的智能水平，以实现对目标物体的自动检测和定位抓取，具有重要的现实意义和研究价值。文章以机器人视觉伺服为研究对象，利用视觉伺服使机器人末端跟踪标定板的位姿，包括摄像机标定、机器人手眼标定、机器人控制，可为机械臂的定位抓取提供帮助。     

内参数异构情况下摄像机平移位置的测定

在手眼系统中,摄像机平移是比较容易实现和经常需要的一种运动.本文首先给出了摄像机调整焦距 后,焦距标定的两个比较简捷的方法.然后,根据平移运动中摄像机获取的两幅不同图像,在焦距不同的情况下,给 出了检测摄像机坐标系平行移动的方向的方法.事实上,摄像机调整焦距也是一种平移运动,因此这时的摄像机的 平移运动是两种平移运动的综合的结果,同时给出了由平台平移引起的摄像机坐标系平移位置的求解方法.     

基于手眼标定方程AX=XB的精度影响因素研究

机器人手位姿数据对手眼标定精度的影响不可忽略,将对基于手眼标定方程AX=XB的精度影响因素进行分析.通过手眼标定仿真和实测实验验证上述两个因素对手眼标定精度的影响与理论分析的一致性.通过仿真与实测实验,总结得出了减小摄像机与靶标间距离、减小机器人手的运动前后到基坐标空间距离的相差距离,可提高手眼标定精度,通过四元数法和矩阵直积法验证了此规律在解AX=XB标定方程时的通用性,并且在摄像机与靶标间距约为230 mm以及机器人手的运动前后到基坐标空间距离的相差距离为3.2401 mm时,手眼标定平移向量相对误差最高精度可达0.0403%.     

利用双目视差理论的摄像机参数标定方法

针对龙眼和荔枝采摘机械手的目标获取问题,文章提出了基于双目立体视觉的摄像机标定的实验系统。实验系统是采用双目视差原理来获取深度信息的标定模型,通过对不同距离的标定板上已知特征点的图片获取和数据处理和分析,确定了两摄像机之间的最佳基线距离,在此基础上进一步验证了采摘目标的深度信息与摄像机的焦距有关的理论,并获得摄像机的合理焦距,为采摘机械手定位提供了基础理论研究依据。     

未标定相机的高精度位姿估计方法

针对未标定相机的位姿估计问题,提出了一种焦距和位姿同时迭代的高精度位姿估计算法。现有的未标定相机的位姿估计算法是焦距和相机位姿单独求解,焦距估计精度较差。提出的算法首先通过现有算法得到相机焦距和位姿的初始参数;然后在正交迭代的基础上推导了焦距和位姿最小化函数,将焦距和位姿同时作为初始值进行迭代计算;最后得到高精度的焦距和位姿参数。仿真实验表明提出的算法在点数为10,噪声标准差为2的情况下,角度相对误差小于1%,平移相对误差小于4%,焦距相对误差小于3%;真实实验表明提出的算法与棋盘标定方法的精度相当。与现有算法相比,能够对未标定相机进行高精度的焦距和位姿估计。     

弱光环境下仓库搬运机器人抓取控制方法

为精准、稳定、可重复地完成抓取动作,研究了弱光环境下基于稳定轻量级网络的仓库搬运机器人抓取控制方法。首先,针对搬运环境弱光图像,基于稳定轻量级编/解码网络提取抓取区域弱光特征并进行融合处理,获得正常光抓取区域特征;其次,在深度分离融合提取层中,通过处理正常光抓取区域特征,重构深层特征,从而恢复特征提取时丢失的细节信息;再次,在网络输出层内输入重构特征,输出仓库搬运机器人手爪的抓取位姿参数;最后,通过手眼标定得到的搬运目标图像,采集相机坐标系与机器人坐标系的坐标转换关系,将抓取位姿参数转换成仓库搬运机器人抓取控制量,完成对仓库搬运机器人的抓取控制。实验证明,该方法可有效提取搬运目标抓取区域特征,并可有效预测仓库搬运机器人抓取位姿,能完成仓库搬运机器人抓取控制,且抓取精度较高。     

基于自适应粒子滤波的摄像机位姿估计方法

提出一种基于自适应粒子滤波的摄像机位姿估计方法。该方法首先利用相邻两帧传递模型的噪声方差动态调整传递模型,接着利用内点统计方法计算粒子权值,在对权值作归一化运算之后,利用粒子加权和计算摄像机位置和姿态。实验结果表明该方法很大程度上提高了基于标识的摄像机位姿估计系统的健壮性与稳定性。     

Inside looking out camera pose estimation for virtual studio

This paper studies the inside looking out camera pose estimation for the virtual studio. The camera pose estimation process, the process of estimating a camera’s extrinsic parameters, is based on closed-form geometrical approaches which use the benefit of simple corner detection of 3D cubic-like virtual studio landmarks. We first look at the effective parameters of the camera pose estimation process for the virtual studio. Our studies include all characteristic landmark parameters like landmark lengths, landmark corner angles and their installation position errors and some camera parameters like lens focal length and CCD resolution. Through computer simulation we investigate and analyze all these parameters’ efficiency in camera extrinsic parameters, including camera rotation and position matrixes. Based on this work, we found that the camera translation vector is affected more than other camera extrinsic parameters because of the noise of effective camera pose estimation parameters. Therefore, we present a novel iterative geometrical noise cancellation method for the closed-form camera pose estimation process. This is based on the collinearity theory that reduces the estimation error of the camera translation vector, which plays a major role in camera extrinsic parameters estimation errors. To validate our method, we test it in a complete virtual studio simulation. Our simulation results show that they are in the same order as those of some commercial systems, such as the BBC and InterSense IS-1200 VisTracker.     

Registration and occlusion handling based on the FAST ICP-ORB method for augmented reality systems

Spatial position consistency and occlusion consistency are two important problems in augmented reality systems. In this paper, we proposed a novel method that can address the registration problem and occlusion problem simultaneously by using an RGB-D camera. First, to solve the image alignment errors caused by the imaging mode of the RGB-D camera, we developed a depth map inpainting method that combines the FMM and RGB-D information. Second, we established an automatic method to judge the close-range mode based on the depth histogram to solve the registration failure problem caused by hardware limitations. In the close-range mode, the registration method combining the fast ICP and ORB was adopted to calculate the camera pose. Third, we developed an occlusion handling method based on the geometric analysis of the scene. Several experiments were performed to validate the performance of the proposed method. The experimental results indicate that our method can obtain stable and accurate registration and occlusion handling results in both the close-range and non-close-range modes. Moreover, the mutual occlusion problem can be handled effectively, and the proposed method can satisfy the real-time requirements of augmented reality systems.

     

双摄像机模组的组合式标定和校正方法

提出了双摄像机模组的组合式标定和校正方法,能够将传统的标定和校正2道工序合并为1道工序,不需要借助于外部测量设备,仅利用双摄像机同时对目标模板拍摄的1幅图像,即可实现双摄像机模组的标定和校正。先基于交比不变性计算摄像机的径向畸变系数,将摄像机畸变成像模型转换为线性模型,利用线性模型分别对2个摄像机进行标定;然后计算2个摄像机之间的位姿偏移参数,调节右摄像机位姿,进行双摄像机之间的位姿校正;最后标定2个摄像机之间的位姿参数。实际应用结果表明,所提出的双摄像机模组校正和标定方法,校正和标定精度高,缩短了工艺时间,提高了工艺效率,能够满足双摄像机模组封装生产工艺的要求。     

Vision Based Position Control for MAVs Using One Single Circular Landmark

This paper presents a real-time vision based algorithm for 5 degrees-of-freedom pose estimation and set-point control for a Micro Aerial Vehicle (MAV). The camera is mounted on-board a quadrotor helicopter. Camera pose estimation is based on the appearance of two concentric circles which are used as landmark. We show that that by using a calibrated camera, conic sections, and the assumption that yaw is controlled independently, it is possible to determine the six degrees-of-freedom pose of the MAV. First we show how to detect the landmark in the image frame. Then we present a geometric approach for camera pose estimation from the elliptic appearance of a circle in perspective projection. Using this information we are able to determine the pose of the vehicle. Finally, given a set point in the image frame we are able to control the quadrotor such that the feature appears in the respective target position. The performance of the proposed method is presented through experimental results.     

基于共面二点一线特征的单目视觉定位

研究了根据点、线混合特征进行单目视觉定位问题,在给定物体坐标系中共面的两个特征点和一条特征直线的条件下,根据它们在像平面上的对应计算相机与物体之间的位姿参数。根据三个特征之间的几何位置关系,分两种情况给出问题求解的具体过程,最终将问题转换成求解一个二次方程问题,真实的工件定位实验验证了方法的有效性。该结果为应用单目视觉进行工件定位提供了一种新方法。     

Study on Visual Positioning Based on Homography for Indoor Mobile Robot

Monocular visual positioning for indoor mobile robot is concerned in this paper.A new visual positioning method based on homography matrix in Euclidean space is proposed.It can calculate the position and pose of the mobile robot according to the intrinsic parameters of camera and two position-known points in a plane.It is very simple and low cost in computation.The experimental results show its effectiveness.     

基于单应性矩阵的室内移动机器人视觉定位研究

Monocular visual positioning for indoor mobile robot is concerned in this paper. A new visual positioning method based on homography matrix in Euclidean space is proposed. It can calculate the position and pose of the mobile robot according to the intrinsic parameters of camera and two position-known points in a plane. It is very simple and low cost in computation. The experimental results show its effectiveness.     

MultiCol Bundle Adjustment: A Generic Method for Pose Estimation,Simultaneous Self-Calibration and Reconstruction for Arbitrary Multi-Camera Systems

In this paper, we present a generic, modular bundle adjustment method for pose estimation, simultaneous self-calibration and reconstruction for multi-camera systems. In contrast to other approaches that use bearing vectors (camera rays) as observations, we extend the common collinearity equations with a general camera model and include the relative orientation of each camera w.r.t to the fixed multi-camera system frame yielding the extended collinearity equations that directly express all image observations as functions of all unknowns. Hence, we can either calibrate the camera system, the cameras, reconstruct the observed scene, and/or simply estimate the pose of the system by including the corresponding parameter block into the Jacobian matrix. Apart from evaluating the implementation with comprehensive simulations, we benchmark our method against recently published methods for pose estimation and bundle adjustment for multi-camera systems. Finally, all methods are evaluated using a 6 degree of freedom ground truth data set, that was recorded with a lasertracker.     

Passivity-based Visual Motion Observer with Panoramic Camera for Pose Control

This paper considers the vision-based estimation and pose control with a panoramic camera via passivity approach. First, a hyperbolic projection of a panoramic camera is presented. Next, using standard body-attached coordinate frames (the world frame, mirror frame, camera frame and object frame), we represent the body velocity of the relative rigid body motion (position and orientation). After that, we propose a visual motion observer to estimate the relative rigid body motion from the measured camera data. We show that the estimation error system with a panoramic camera has the passivity which allows us to prove stability in the sense of Lyapunov. The visual motion error system which consists of the estimation error system and the pose control error system preserves the passivity. After that, stability and L ₂-gain performance analysis for the closed-loop system are discussed via Lyapunov method and dissipative systems theory, respectively. Finally, simulation and experimental results are shown in order to confirm the proposed method.