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。     

基于特征分解的快速位姿图优化算法

位姿图优化（pose graph optimization,PGO）是计算机视觉领域中广泛应用的高维非凸优化算法,很难直接求解,主要依赖于迭代技术,对初始值的质量要求较高,在实践中很难得到保证。针对位姿图优化问题进行了研究,提出了基于特征分解的位姿图简单封闭解算法,该算法首先对PGO问题的最大似然估计进行半定松弛,然后将其转换为特征分解问题,并利用数据的稀疏性设计了改进的模型降阶方法进行求解,进一步提高了算法的计算速度。算法具有可伸缩性、计算成本低和精度高等优点。最后,在模拟和真实的位姿图数据集上进行实验评估,结果表明在不影响精度的情况下,该算法可以快速地进行位姿图优化。     

基于EIV模型的点线位姿估计

针对基于多个点线特征的位姿估计问题,该文将点线特征约束统一成二次型约束的形式,并引入EIV模型描述影响点线特征投影的噪声。为了简化问题,采用伪线性化方法,并提出了基于奇异值分解的迭代算法来估计位姿参数。实验表明该算法受初始值影响小,收敛快,能够提供高精度和鲁棒的位姿估计结果。     

双目视觉测量系统位姿解算方法研究

在大部件自动对准系统中,为了解决近距离观测无法获得被观测目标全部特征的问题,使用双目视觉位姿测量解算方法,设置其中一个相机为基准相机,利用两相机之间已知的位姿关系,将非基准相机拍摄的特征点信息转换到基准相机的相机坐标系下,然后利用位姿解算的迭代算法思想求解两部件之间的位姿关系.当特征点数目较少时,迭代算法的误差函数会陷入局部极小值,针对上述问题,设计了一种利用各坐标系之间的转换关系求解迭代初始值的方法,并通过仿真证明了算法的有效性.     

基于迭代最近点的井下无人机实时位姿估计

针对煤矿井下无GPS信号、低照度、结构化环境等特点,提出了一种基于迭代最近点(ICP)的井下无人机实时位姿估计方法。通过建立四旋翼无人机运动模型与机载激光雷达观测模型,将煤矿井下四旋翼无人机位姿估计问题转换为机载激光点云数据的扫描匹配问题。用三维激光雷达作为四旋翼无人机机载环境测量传感器,得到无人机当前位姿下的观测点云数据;以第1帧位置为初始位,通过ICP方法得到连续2帧点云数据之间的相对变换矩阵,迭代求解连续关键帧点云数据,得到煤矿井下四旋翼无人机实时位姿估计结果。采用滤波与下采样方法对点云数据进行优化,加速变换矩阵的求解,满足四旋翼无人机位姿估计实时性需求。实验结果表明,基于ICP的井下无人机实时位姿估计方法能够快速、有效地求解四旋翼无人机位姿,相比于正态分布变换方法,ICP方法更适用于煤矿井下四旋翼无人机的实时位姿估计。     

基于非线性优化的长宽比未知矩形的位姿测量

提出一种新的基于非线性优化的长宽比未知矩形的位姿测量算法．该算法利用矩形顶点对应的4个像点,可有效地估计出矩形长宽比,并且给出矩形目标跟相机之间的相对姿态以及在矩形边长尺度下的相对位置．在该算法中,为了消除相对位姿和长宽比对计算精度的相互影响,将原问题分解为求解相对位姿和求解长宽比两个子问题．对求解相对姿态子问题建立关于相对位姿的代价函数,采用SE（3）李群的方法对相对位姿进行表示,通过优化代价函数对位姿进行求解;对求解长宽比子问题采用二分法搜索最优值的求解方法．通过实验验证了该算法在使用4个像点进行长宽比估计时的有效性和实时性．其相对计算误差小于3%,而且算法在普通PC上可以满足实时测量位姿的需求．     

基于改进的3维ICP匹配的单目视觉里程计

针对目前流行的单目视觉里程计当移动机器人做“近似纯旋转运动”时鲁棒性不强的问题,从理论上分析了其定位鲁棒性不高的原因,提出了一种基于改进的3维迭代最近点（ICP）匹配的单目视觉里程计算法．该算法首先初始化图像的边特征点对应的深度值,之后利用改进的3维ICP算法迭代求解2帧图像之间对应的3维坐标点集的6维位姿,最后结合边特征的几何约束关系利用扩展卡尔曼深度滤波器更新深度值．改进的ICP算法利用反深度不确定度加权、边特征梯度搜索与匹配等方法,提高了传统ICP算法迭代求解的实时性和准确性．并且将轮子里程计数据作为迭代初始值,能够进一步提高定位算法的精度和针对“近似纯旋转运动”问题的鲁棒性．本文采用3个公开数据集进行算法验证,该算法在不损失定位精度的前提下,能够有效提高针对近似纯旋转运动、大场景下的鲁棒性．单目移动机器人利用本文算法可在一定程度上校正里程计漂移的问题．     

增强现实环境下复杂板材的三维位姿跟踪方法

针对增强现实(AR)技术在实际工业生产场景应用中遇到的实时目标监测、复杂场景识别等问题,提出基于线特征求解板材三维位姿变化量的目标位姿跟踪方法,在单目摄像头环境下即可实现复杂表面板材的位姿监测与拟合。在利用头顶摄像头检测工件状态后,首先将当前帧图像进行降采样,再在HSV空间对进行图像预处理,并利用形态学梯度和改进的直线分割检测(LSD)直线算法提取边缘直线,然后结合模板图像的边缘直线以及利用多项式回归拟合关系曲线的方法求解出天线板的位姿变化量。实验结果表明:所提的基于改进的LSD算法提取线特征位姿监测方法相比基于ORB特征点和纹理特征位姿跟踪方法具有求解速度快、鲁棒性好等优点。     

一种新的基于直线的定位方法

我们通常采用任意摆放位置的三条直线来完成三维物体定位. 对于交于两点的三条非共面直线这种配置, 它具有位置任意摆放三条直线不具有的一些特殊性质. 因此, 我们基于这种特殊配置直线提出一种确定物体位姿的新方法. 在理论上, 这种方法是对基于三条直线位姿估计方法的丰富, 同时这种方法也能为实际工程应用提供支持. 而且, 我们提出一种解决多解现象的方法和一种新的迭代方法. 最后, 仿真实验表明我们的算法具有较快的工作速度和较好的鲁棒性.     

基于kinect的改进RGB-D视觉里程计

针对RGB-D视觉里程计中kinect相机所捕获的图像深度区域缺失的问题,提出了一种基于PnP(perspective-n-point)和ICP(iterative closest point)的融合优化算法。传统ICP算法迭代相机位姿时由于深度缺失,经常出现特征点丢失导致算法无法收敛或误差过大。本算法通过对特征点的深度值判定,建立BA优化模型,并利用g2o求解器进行特征点与相机位姿的优化。实验证明了该方法的有效性,提高了相机位姿估计的精度及算法的收敛成功率,从而提高了RGB-D视觉里程计的精确性和鲁棒性。     

Pose Estimation using Point and Line Correspondences

The problem of a real-time pose estimation between a 3D scene and a single camera is a fundamental task in most 3D computer vision and robotics applications such as object tracking, visual servoing, and virtual reality. In this paper we present two fast methods for estimating the 3D pose using 2D to 3D point and line correspondences. The first method is based on the iterative use of a weak perspective camera model and forms a generalization of DeMenthon's method (1995) which consists of determining the pose from point correspondences. In this method the pose is iteratively improved with a weak perspective camera model and at convergence the computed pose corresponds to the perspective camera model. The second method is based on the iterative use of a paraperspective camera model which is a first order approximation of perspective. We describe in detail these two methods for both non-planar and planar objects. Experiments involving synthetic data as well as real range data indicate the feasibility and robustness of these two methods. We analyse the convergence of these methods and we conclude that the iterative paraperspective method has better convergence properties than the iterative weak perspective method. We also introduce a non-linear optimization method for solving the pose problem.     

Three-Dimensional Reconstruction of Points and Lines with Unknown Correspondence across Images

Three-dimensional reconstruction from a set of images is an important and difficult problem in computer vision. In this paper, we address the problem of determining image feature correspondences while simultaneously reconstructing the corresponding 3D features, given the camera poses of disparate monocular views. First, two new affinity measures are presented that capture the degree to which candidate features from different images consistently represent the projection of the same 3D point or 3D line. An affinity measure for point features in two different views is defined with respect to their distance from a hypothetical projected 3D pseudo-intersection point. Similarly, an affinity measure for 2D image line segments across three views is defined with respect to a 3D pseudo-intersection line. These affinity measures provide a foundation for determining unknown correspondences using weighted bipartite graphs representing candidate point and line matches across different images. As a result of this graph representation, a standard graph-theoretic algorithm can provide an optimal, simultaneous matching and triangulation of points across two views, and lines across three views. Experimental results on synthetic and real data demonstrate the effectiveness of the approach.An erratum to this article can be found at     

Pose Estimation in Conformal Geometric Algebra Part II: Real-Time Pose Estimation Using Extended Feature Concepts

Part II uses the foundations of Part I [35] to define constraint equations for 2D-3D pose estimation of different corresponding entities. Most articles on pose estimation concentrate on specific types of correspondences, mostly between points, and only rarely use line correspondences. The first aim of this part is to extend pose estimation scenarios to correspondences of an extended set of geometric entities. In this context we are interested to relate the following (2D) image and (3D) model types: 2D point/3D point, 2D line/3D point, 2D line/3D line, 2D conic/3D circle, 2D conic/3D sphere. Furthermore, to handle articulated objects, we describe kinematic chains in this context in a similar manner. We ensure that all constraint equations end up in a distance measure in the Euclidean space, which is well posed in the context of noisy data. We also discuss the numerical estimation of the pose. We propose to use linearized twist transformations which result in well conditioned and fast solvable systems of equations. The key idea is not to search for the representation of the Lie group, describing the rigid body motion, but for the representation of their generating Lie algebra. This leads to real-time capable algorithms.Bodo Rosenhahn gained his diploma degree in Computer Science in 1999. Since then he has been pursuing his Ph.D. at the Cognitive Systems Group, Institute of Computer Science, Christian-Albrechts University Kiel, Germany. He is working on geometric applications of Clifford algebras in computer vision.Prof. Dr. Gerald Sommer received a diploma degree in physics from the Friedrich-Schiller-Universität Jena, Germany, in 1969, a Ph.D. degree in physics from the same university in 1975, and a habilitation degree in engineering from the Technical University Ilmenau, Germany, in 1988. Since 1993 he is leading the research group Cognitive Systems at the Christian-Albrechts-Universität Kiel, Germany. Currently he is also the scientific coordinator of the VISATEC project.     

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。     

Robust and Accurate Monocular Visual Navigation Combining IMU for a Quadrotor

In this paper, we present a multi-sensor fusion based monocular visual navigation system for a quadrotor with limited payload, power and computational resources. Our system is equipped with an inertial measurement unit (IMU), a sonar and a monocular down-looking camera. It is able to work well in GPS-denied and markerless environments. Different from most of the keyframe-based visual navigation systems, our system uses the information from both keyframes and keypoints in each frame. The GPU-based speeded up robust feature (SURF) is employed for feature detection and feature matching. Based on the flight characteristics of quadrotor, we propose a refined preliminary motion estimation algorithm combining IMU data. A multi-level judgment rule is then presented which is beneficial to hovering conditions and reduces the error accumulation effectively. By using the sonar sensor, the metric scale estimation problem has been solved. We also present the novel IMU+3P (IMU with three point correspondences) algorithm for accurate pose estimation. This algorithm transforms the 6-DOF pose estimation problem into a 4-DOF problem and can obtain more accurate results with less computation time. We perform the experiments of monocular visual navigation system in real indoor and outdoor environments. The results demonstrate that the monocular visual navigation system performing in real-time has robust and accurate navigation results of the quadrotor.      

The 3D Line Motion Matrix and Alignment of Line Reconstructions

We study the problem of aligning two 3D line reconstructions in projective, affine, metric or Euclidean space.We introduce the 6 × 6 3D line motion matrix that acts on Plücker coordinates. We characterize its algebraic properties and its relation to the usual 4 × 4 point motion matrix, and propose various methods for estimating 3D motion from line correspondences, based on cost functions defined in images or 3D space. We assess the quality of the different estimation methods using simulated data and real images.     

Branch-and-Bound Methods for Euclidean Registration Problems

In this paper, we propose a practical and efficient method for finding the globally optimal solution to the problem of determining the pose of an object. We present a framework that allows us to use point-to-point, point-to-line, and point-to-plane correspondences for solving various types of pose and registration problems involving euclidean (or similarity) transformations. Traditional methods such as the iterative closest point algorithm or bundle adjustment methods for camera pose may get trapped in local minima due to the nonconvexity of the corresponding optimization problem. Our approach of solving the mathematical optimization problems guarantees global optimality. The optimization scheme is based on ideas from global optimization theory, in particular convex underestimators in combination with branch-and-bound methods. We provide a provably optimal algorithm and demonstrate good performance on both synthetic and real data. We also give examples of where traditional methods fail due to the local minima problem.     

Annotated face model-based alignment: a robust landmark-free pose estimation approach for 3D model registration

Registering a 3D facial model onto a 2D image is important for constructing pixel-wise correspondences between different facial images. The registration is based on a 3 \(\times \) 4 dimensional projection matrix, which is obtained from pose estimation. Conventional pose estimation approaches employ facial landmarks to determine the coefficients inside the projection matrix and are sensitive to missing or incorrect landmarks. In this paper, a landmark-free pose estimation method is presented. The method can be used to estimate the matrix when facial landmarks are not available. Experimental results show that the proposed method outperforms several landmark-free pose estimation methods and achieves competitive accuracy in terms of estimating pose parameters. The method is also demonstrated to be effective as part of a 3D-aided face recognition pipeline (UR2D), whose rank-1 identification rate is competitive to the methods that use landmarks to estimate head pose.