Laser range scanner based on self-calibration techniques using coplanarities and metric constraints

In this paper, we propose a novel method to achieve both dense 3D reconstruction of the scene and estimation of the camera intrinsic parameters by using coplanarities and other constraints (e.g., orthogonalities or parallelisms) derived from relations between planes in the scene and reflected curves of line lasers captured by a single camera. In our study, we categorize coplanarities in the scene into two types: implicit coplanarities, which can be observed as reflected curves of line lasers, and explicit coplanarities, which are, for example, observed as walls of a building. By using both types of coplanarities, we can construct simultaneous equations and can solve them up to four degrees of freedom. To upgrade the solution to the Euclidean space and estimate the camera intrinsic parameters, we can use metric constraints such as orthogonalities of the planes. Such metric constraints are given by, for example, observing the corners of rectangular boxes in the scene, or using special laser projecting device composed of two line lasers whose laser planes are configured to be perpendicular.     

用于高精度三维计算机视觉的图象系统标定和误差补偿

为了对计算机视觉系统进行标定,并削弱由系统模型与真实系统间差异所产生的误差,在详细分析计算机数字成象过程的基础上,提出了一种综合数字成象过程中内部的及外部的,已知的及未知的等各种因素的图象系统标定方法,其标定的具体对象包括光学成象过程中的线性几何投影参数及非线性畸变参数、光电转换过程中的参数和图象信号的D／A及A／D转换过程中的参数等;同时提出了基于该标定方法的三维计算机视觉系统采集三维数据的误差补偿方法及其参数的确定过程。上述方法已经成功地应用于三维计算机视觉系统,并取得了很好的三维精度。     

基于单个读数头的角位移传感器自标定方法

在保证角位移传感器自标定精度的前提下,为克服多读数头安装的一致性问题,同时尽可能避免对标定系统硬件上的苛刻要求,提出一种基于单个读数头,借助测量数据微商的傅里叶级数,实现角位移传感器自标定的方法。推导了该自标定方法引入的误差,并据此提出一种对应的优化方法。将其应用于时栅角位移传感器的标定中,实验结果表明,在相同测量环境下,该自标定方法的标定精度接近借助外部参考基准的传统标定方法,达到约±0.8″;优化后的自标定方法能够有效提高标定精度与标定效率,并且在大微元长度快速标定时,其标定精度可优于传统标定方法接近5倍。     

Shape Reconstruction and Camera Self-Calibration Using Cast Shadows and Scene Geometries

Recently, various techniques of shape reconstruction using cast shadows have been proposed. These techniques have the advantage that they can be applied to various scenes, including outdoor scenes, without using special devices. Previously proposed techniques usually require calibration of camera parameters and light source positions, and such calibration processes limit the range of application of these techniques. In this paper, we propose a method to reconstruct 3D scenes even when the camera parameters or light source positions are unknown. The technique first recovers the shape with 4-DOF indeterminacy using coplanarities obtained by cast shadows of straight edges or visible planes in a scene, and then upgrades the shape using metric constraints obtained from the geometrical constraints in the scene. In order to circumvent the need for calibrations and special devices, we propose both linear and nonlinear methods in this paper. Experiments using simulated and real images verified the effectiveness of this technique.     

传统相机标定方式的自动标定方法

摄像机标定技术是从二维平面图像中获得三维信息的关键步骤,摄像机的标定精度对保证系统测量精度有着至关重要的作用。提出了一种基于传统相机标定方式的自动标定方法。采用带有区域标记点的改进标定模板获取标定图像序列。利用边缘检测技术,确定特征点检测区域。根据摄像机透视投影模型,对摄相机进行线性标定,再进行非线性修正,得出摄像机各参数。实验结果表明,改进的标定方法具有更快的检测速度,稳定性好,鲁棒性高,使传统相机标定过程实现了自动化。     

Automatic registration of laser reflectance and colour intensity images for 3D reconstruction

The objective of the work presented in this paper is to generate complete, high-resolution models of real world scenes from passive intensity images and active range sensors. In previous work, an automatic method has been developed in order to compute 3D models of real world scenes from laser range data. The aim of this project is to improve these existing models by fusing range and intensity data. The paper presents different techniques in order to find correspondences between the different sets of data. Based on these control points, a robust camera calibration is computed with a minimal user intervention in order to avoid the fastidious point and click phase that is still necessary in many systems. The intensity images are then re-projected into the laser coordinate frame to produce an image that combines the laser reflectance and the available video intensity images into a colour texture map.     

CC—Link现场总线的通信初始化方法及应用

通过对两种CC—Link现场总线数据通信初始化实现方法优缺点的分析,在后处理设备控制系统的设计中采用了网络参数方法来实现CC—Link通信初始化,从而大大地减少了工作量并提高了设计效率。本文详细介绍了PLC系统的硬件配置、模块状态、网络参数及自动刷新参数的设置。     

一种高效的自组织特征映射图的初始化方法

自组织特征映射图算法(SOFM,self-organizing Feature Map)在模式识别中有着广泛的应用.本文首先讨论了网络结构的初始化设置对自组织特征映射图构造的影响以及加速SOFM网络学习训练过程的主要方法,然后提出一种从边界到中心的自组织特征映射图初始化方法,该方法形成的自组织特征映射图能够真实地表示输入样本内在关系,大大减少学习训练次数,从而有效改进了传统的SOFM算法.     

Acquisition of three-dimensional information of brain structures using endoneurosonography

Recent trends in minimally invasive brain surgery aim at using the joint acquisition of endoscopic and ultrasound images, a technique called endoneurosonography (ENS). Endoscopic images are useful, but they cannot provide information beyond opaque structures; therefore, it is necessary to complement them with other medical images. In this sense, ultrasound images provide a good option because they are cheap and easy to acquire in intraoperative scenarios.This paper presents a methodology for acquiring 3D information on brain structures using endoneurosonography. The endoscopic cameras and the ultrasound sensor are inserted into the brain during surgery. The information from the endoscopic cameras is used for stereo reconstruction, while the ultrasound information is used to segment regions of interest. The aim of this work is solving four specific problems: calibration of the endoscopic system; tracking of the ultrasound probe through endoscopic images; segmentation of brain structures from ultrasound images; and 3D reconstruction of brain tumors. At the end of the paper, the complete methodology is explained and experiments are done on an ENS database of brain phantoms.     

A robust method to recognize critical configuration for camera calibration

When space points and camera optical center lie on a twisted cubic, no matter how many pairs there are used from the space points to their image points, camera parameters cannot be determined uniquely. This configuration is critical for camera calibration. We set up invariant relationship between six space points and their image points for the critical configuration. Then based on the relationship, an algorithm to recognize the critical configuration of at least six pairs of space and image points is proposed by using a constructed criterion function, where no any explicit computation on camera projective matrix or optical center is needed. Experiments show the efficiency of the proposed method.     

Robust refinement methods for camera calibration and 3D reconstruction from multiple images

This paper proposes robust refinement methods to improve the popular patch multi-view 3D reconstruction algorithm by Furukawa and Ponce (2008). Specifically, a new method is proposed to improve the robustness by removing outliers based on a filtering approach. In addition, this work also proposes a method to divide the 3D points in to several buckets for applying the sparse bundle adjustment algorithm (SBA) individually, removing the outliers and finally merging them. The residuals are used to filter potential outliers to reduce the re-projection error used as the performance evaluation of refinement. In our experiments, the original mean re-projection error is about 47.6. After applying the proposed methods, the mean error is reduced to 2.13.     

微操作机器人系统三维标定的研究与实现

提出三维标定方法 ,提出用这种方法标定的精度主要取决于三维标定块的加工精度和标定过程中对标定块的定位精度 ,给出实现三维标定的关键技术     

Volumetric Model Reconstruction from Unrestricted Camera Views Based on the Photo-consistency of 3D Voxel Mask

This project aims to develop a three-dimensional (3D) model reconstruction system using images acquired from a mobile camera. It consists of four major steps: camera calibration, volumetric model reconstruction, surface modeling and texture mapping. A novel online scale factor estimation is developed to enhance the accuracy of the coplanar camera calibration. For the volumetric modeling, the voting-based shape-from-silhouette first generates a coarse model, which is then refined by the photo-consistency check using the novel 3D voxel mask. Our scheme can handle concave surface in a sophisticated way. Finally, the surface model is formed with the original images mapped. 3D models of some test objects are presented.     

基于投影仪和摄像机的结构光视觉标定方法

为实现基于投影仪和摄像机的结构光视觉系统连续扫描,需要计算投影仪投影的任意光平面与摄像机图像平面的空间位置关系,进而需要求取摄像机光心与投影仪光心之间的相对位置关系。求取摄像机的内参数,在标定板上选取四个角点作为特征点并利用摄像机内参数求取该四个特征点的外参数,从而知道四个特征点在摄像机坐标系中的坐标。利用投影仪自身参数求解特征点在投影仪坐标系中的坐标,从而计算出摄像机光心与投影仪光心之间的相对位置关系,实现结构光视觉标定。利用标定后的视觉系统,对标定板上的角点距离进行测量,最大相对误差为0.277%,表明该标定算法可以应用于基于投影仪和摄像机的结构光视觉系统。     

Simple and efficient method of calibrating a motorized zoom lens

In this work, three servo motors are used to independently control the aperture, zoom, and focus of our zoom lens. Our goal is to calibrate, efficiently, the camera parameters for all the possible configurations of lens settings. We use a calibration object suitable for zoom lens calibration to deal with the defocusing problem. Instead of calibrating the zoom lens with respect to the three lens settings simultaneously, we perform the monofocal camera calibration, adaptively, over the ranges of the zoom and focus settings while fixing the aperture setting at a preset value. Bilinear interpolation is used to provide the values of the camera parameters for those lens settings where no observations are taken. The adaptive strategy requires the monofocal camera calibration only for the lens settings where the interpolated camera parameters are not accurate enough, and is hence referred to as the calibration-on-demand method. Our experiments show that the proposed calibration-on-demand method can provide accurate camera parameters for all the lens settings of a motorized zoom lens, even though the camera calibration is performed only for a few sampled lens settings.     

Tracking-based depth recovery for virtual reality applications

This paper describes a technique for tracking-based reconstruction from camera images. The position and orientation data are provided by a commercial laser tracker rigidly attached to the camera. The tracker enables the determination of the extrinsic camera parameters. Focal length is automatically calibrated in parallel with the depth computation process by means of a Kalman filter. In addition to its simplicity, our technique enables the fast generation of three-dimensional models from two-dimensional images to be used in virtual reality applications.Received: 16 August 2003, Revised: 13 August 2004, Published online: 20 December 2004 Correspondence to: Ali Akgunduz     

An improved algorithm for two-image camera self-calibration and Euclidean structure recovery using absolute quadric

We present an improved algorithm for two-image camera self-calibration and Euclidean structure recovery, where the effective focal lengths of both cameras are assumed to be the only unknown intrinsic parameters. By using the absolute quadric, it is shown that the effective focal lengths can be computed linearly from two perspective images without imposing scene or motion constraints. Moreover, a quadratic equation derived from the absolute quadric is proposed for solving the parameters of the plane at infinity from two images, which upgrades a projective reconstruction to a Euclidean reconstruction.     

Revisiting Zhang's 1D calibration algorithm

In recent years, the camera calibration using 1D patterns has been studied and improved by researchers all over the world. However, the progress in that area has been mainly in the sense of reducing the restrictions to the 1D pattern movement. On the other hand, the method's accuracy still demands improvements. In the present paper, the original technique proposed by Zhang is revisited and we demonstrate that the method's accuracy can be significantly improved, simply by analyzing and reformulating the problem. The numerical conditioning can be improved if a simple data normalization is performed. Furthermore, a non-linear solution based on the Partitioned Levenberg-Marquardt algorithm is proposed. That solution takes advantage of the problem's particular structure to reduce the computational complexity of the original method and to improve the accuracy. Tests using both synthetic and real images demonstrate that the calibration method using 1D patterns can be applied in practice, with accuracy comparable to other already traditional methods.     

Direct Calibration by Fitting of Cuboids to a Single Image Using Differential Evolution

In this article we propose a new method to calibrate directly the camera by which it was taken an image of a cuboid, and to find at the same time the orientation and side lengths of the cuboid. This is a highly non-linear optimization problem that is solved directly using a heuristic called differential evolution. We show in this paper that this problem is very difficult if one tries to solve it with a conventional scalar optimization procedure. Although differential evolution is a heuristic, we find valid results in 100% of the executions. We test our method with synthetic and real images.     

Straight lines have to be straight

Most algorithms in 3D computer vision rely on the pinhole camera model because of its simplicity, whereas video optics, especially low-cost wide-angle or fish-eye lenses, generate a lot of non-linear distortion which can be critical. To find the distortion parameters of a camera, we use the following fundamental property: a camera follows the pinhole model if and only if the projection of every line in space onto the camera is a line. Consequently, if we find the transformation on the video image so that every line in space is viewed in the transformed image as a line, then we know how to remove the distortion from the image. The algorithm consists of first doing edge extraction on a possibly distorted video sequence, then doing polygonal approximation with a large tolerance on these edges to extract possible lines from the sequence, and then finding the parameters of our distortion model that best transform these edges to segments. Results are presented on real video images, compared with distortion calibration obtained by a full camera calibration method which uses a calibration grid. Received: 27 December 1999 / Accepted: 8 November 2000