仿生眼运动视觉与立体视觉3维感知

为使机器人同时具备双目立体视觉和单目运动视觉的仿人化环境感知能力,克服双目视场狭窄、单目深度感知精度低的缺陷,本文基于人眼结构特点,设计了一个具有4个旋转自由度的双目仿生眼平台,并分别基于视觉对准策略和手眼标定技术实现了该平台的初始定位和参数标定.给出了基于外部参数动态变化的双目立体感知方法和单目运动立体感知方法,前者通过两架摄像机实时获取的图像信息以及摄像机相对位姿信息进行3维感知,后者综合利用单个摄像机在多个相邻时刻获取的多个图像及其对应姿态进行3维感知.实验结果中的双目视觉相对感知精度为0.38%,单目运动视觉相对感知精度为0.82%.本文方法不但能够有效拓宽传统双目视觉的感知视野,而且能够保证双目感知和单目运动感知的准确性.     

大场景中物体运动轨迹的测量

提出一种基于旋转摄像机拍摄的物体运动轨迹测量方法。该方法采用旋转摄像机跟踪拍摄运动物体,通过摄像机标定技术建立图像平面与物体运动平面的对应关系,完成对物体运动轨迹的测量。该测量方法解决了单台摄像机拍摄范围和拍摄精度的约束,与多摄像机协同拍摄相比,需要的测量设备少,容易操作,对场地的适应能力强。     

基于单目CCD摄像机的三维点云数据重建研究

提出一种基于单目CCD摄像机反求系统的三维数据的获取方法熏首先对单目CCD摄像机拍摄的单幅图像进行滤波、细化等手段处理得到单像素宽度的激光条纹熏通过摄像机标定和激光投影平面标定建立激光条纹上的像素点与空间上的三维点之间一一对应的函数映射,获取物体的外轮廓信息。通过试验分析证明其可行性。     

基于主动视觉系统的摄像机自定标方法研究

提出了一种新的基于主动视觉系统的摄像机自定标方法,其主要特点是可以线性求解 摄像机的所有5个内参数.该方法的基本原理是利用图像中平面场景的信息,通过控制摄像机 作多组平面正交平移运动,由平面场景图像的单应性(homography)矩阵建立摄像机内参数线性 约束方程组,来求解摄像机的内参数,同时给出约束方程组解的唯一性与平移运动组之间的关系.     

基于全局彩色摄像机的移动机器人定位

本文提出一种基于全局彩色摄像机的移动机器人定位方法。该方法首先利用全局彩色摄像机对移动机器人所在的运动平面拍摄图像并基于色调和饱和度信息进行图像分割获取目标物体在图像平面坐标系中的位置,然后利用多项式拟合的方法实现图像平面坐标系到移动机器人运动平面坐标系的映射,从而确定移动机器人在其运动平面坐标系中的位置,同时利用移动机器人的行驶轨线近似获得其方向角。实验结果表明了上述方法的有效性,具有一定的实用价值。     

一种基于图像序列和外部平移数据的摄像机自定标方法

摄像机标定是从二维图像获取三维信息必不可少的步骤。本文提出了一种新的基于图像序列的自定标方法。通过控制摄像机平台作3次平移运动（其中任意2次均不在同一平面上）且不需要读取运动平台的平移数据，即可线性地标定摄像机的内参数。大量的模拟实验表明该算法精度高和鲁棒性强。     

增量式参数化特征模型及其单目视觉导航

为了解决单目摄像机6自由度运动参数估计中的尺度不确定问题,提出了场景特征深度的“增量式参数化”模型.和传统的欧几里得参数化模型和逆深度参数化模型不同,在该模型中,图像序列特征点仅仅由其深度特征进行增量式描述.此外,引入了非线性系统的状态可观测性理论,对不同参数化模型进行了可观测性分析,证明了“增量式参数化模型”的一致可观测性.最后采用室内移动摄像机采集的公共数据集,进行了室内单目视觉导航实验,实验结果证明了该增量式参数化模型能够显著改善单目视觉导航性能.     

利用机场现有设施的机载单目视觉助降测量方法

为了满足航空器在降落导航中的数据需求,对利用机场现有设施（跑道、信号灯等）的机载单目视觉助降测量方法进行了研究,并推导出了相应的投影关系。当航空器飞行至机场附近时,只要机载单目摄像机能够拍摄到若干现有机场设施且已知这些点线特征的大地坐标,即可在图像上提取相应的点线特征,并依据投影关系测量计算出拍摄时刻航空器的位置和姿态参数。由于不需要人为在机场附近布置测量标志物,极大地提高了方法应用的灵活性。仿真结果证明方法有效可行。     

一种新的线性摄像机自标定方法

提出了一种新的基于主动视觉系统的线性摄像机自定标方法。所谓基于主动视觉系统,是指摄像机固定在摄像机平台上以平摄像机平台的运动可以精确控制。该方法的主要特点是可以线性求解摄像机的所有5个内参数。据作者所知。文献中现有的方法仅能线性求解摄像机的4个由参数。当摄像机为完全的射影模型时,即当有畸变因子（skew factor）存在时,文献中的线性方法均不再适用。该方法的基本思想是控制摄像机做5组平面正交运动,利用图像中的极点（epipoles）信息来线性标定摄像机。同时,针对摄像机做平移运动时基本矩阵的特殊形式,该文提出了求基本矩阵（fundamental matrix)的2点算法。与8点算法相比较,2点算法大大提高了所求极点的精度和鲁棒性。另外,该文对临近奇异状态（即5组平面正交运动中,有两组或者多组运动平面平行）作了较为详尽的分析,并提出了解决临近奇异状态的策略,从而增强了该文算法的衫性。模拟图像和真实图像实验表明该文的自标定方法具有较高的鲁棒性和准确性。     

轮式移动机器人单目视觉的目标测距方法

为减小特征匹配以及摄像机光轴倾斜对单目测距造成的误差,提出一种基于单目视觉的轮式移动机器人目标测距方法,将测距目标从平面物体推广到立体物体,在无须校正的情况下提高测量精度。通过对摄像机进行标定测量其内外参数,建立小孔平面成像模型得到世界坐标系与像素坐标系的对应关系。针对坐标变换矩阵奇异的情况,引入面积这一特性进行求解,推导特定矩阵奇异情况下目标距离与像素面积的关系。实验结果表明,该方法可将综合误差率控制在0.7%以内,能够满足轮式移动机器人单目视觉测距实时性和可靠性的要求。     

小型无人直升机着陆高度单目视觉测量方法

针对小型无人直升机自主降落过程中姿态发生变化和非差分GPS高程精度不高的情况,研究了一种基于单目视觉的小型无人直升机离地高度测量方法,用于提高自主降落的品质。在机体姿态角已知的情况下,识别提取降落图标中相互平行的特征直线段,并根据机载摄像头投影模型,实时求解机体降落过程中的离地高度。在测量系统上进行了实体实验,验证了所提出的方法,实验结果表明:该方法特征识别率高,在机体平面与地面存在倾角时,输出结果的实时性与准确性亦均能满足小型无人直升机自主降落要求。     

基于对应线标定的无人机着陆位姿确定算法

为提高无人机着陆阶段导航信息的自主性,研究了一种基于对应线标定的单目视觉位姿测量方法,以独立确定固定翼无人机在着陆时的姿态和相对着陆点的位置。首先,建立了机载摄像机的运动和投影模型;然后,利用着陆场上任意3条已知自然直线特征,依据对应线标定法建立关于无人机姿态和相对位置的约束方程,以进行无人机姿态和相对位置的解算;最后,进行了地面静态模拟实验,实验结果表明:姿态角误差控制在3°内,位置误差控制在3%内,可以满足某型无人机着陆阶段对导航信息的要求,并且,该方法充分利用了着陆场自然直线特征,贴近实战需要。     

Homography-based depth recovery with descent images

In planetary landing exploration task, the images captured by the landing camera are nearly along optical axis which results in multi-resolution images of same terrain surface. Recovering the surface shape of landing terrain from descent imagery is of great value for lander to choose safe landing area. In this paper, a homography-based depth recovery method with descent images is addressed. At first, the parallax and scale change in descent images are analyzed. Second, the camera motion is optimized with SIFT features correspondence constraints. For dense depth recovery, a set of virtual parallel planes is assumed to slice the terrain and each plane induces a homography to warp back the second image to first image plane. Zero-normalized cross-correlation score is chosen to compute the correlation score and the correlation curve is smoothed by two Gaussian filters. The depth for each pixel is determined by the plane which has highest correlation value. At the end, some experiments are conducted, including different correlation computation, depth recovery with different terrain, and the error tests. The results show that the discussed method is feasible to recover the depth information overall.     

Less restrictive camera odometry estimation from monocular camera

This paper addresses the problem of estimating a camera motion from a non-calibrated monocular camera. Compared to existing methods that rely on restrictive assumptions, we propose a method which can estimate camera motion with much less restrictions by adopting new example-based techniques compensating the lack of information. Specifically, we estimate the focal length of the camera by referring to visually similar training images with which focal lengths are associated. For one step camera estimation, we refer to stationary points (landmark points) whose depths are estimated based on RGB-D candidates. In addition to landmark points, moving objects can be also used as an information source to estimate the camera motion. Therefore, our method simultaneously estimates the camera motion for a video, and the 3D trajectories of objects in this video by using Reversible Jump Markov Chain Monte Carlo (RJ-MCMC) particle filtering. Our method is evaluated on challenging datasets demonstrating its effectiveness and efficiency.     

基于单视频图像序列的人体三维姿态重建

提出了至少存在一个深度值已知点的约束条件下,基于单视频图像序列重建人体三维姿态的方法.利用已知间距的平面点阵来标定获得摄像机参数,在透视投影模型下,根据单视频图像序列中人体关节点的二维数据,重建其三维信息.并将人体运动序列按照运动突变点划分为若干子序列,有效消除了二义性的干扰,较为精确的实现了人体三维姿态的重建.给出了该方法的实验过程及计算结果,验证了该算法的可行性和精度.     

Real time UAV altitude, attitude and motion estimation from hybrid stereovision

Knowledge of altitude, attitude and motion is essential for an Unmanned Aerial Vehicle during critical maneuvers such as landing and take-off. In this paper we present a hybrid stereoscopic rig composed of a fisheye and a perspective camera for vision-based navigation. In contrast to classical stereoscopic systems based on feature matching, we propose methods which avoid matching between hybrid views. A?plane-sweeping approach is proposed for estimating altitude and detecting the ground plane. Rotation and translation are then estimated by decoupling: the fisheye camera contributes to evaluating attitude, while the perspective camera contributes to estimating the scale of the translation. The motion can be estimated robustly at the scale, thanks to the knowledge of the altitude. We propose a robust, real-time, accurate, exclusively vision-based approach with an embedded C++ implementation. Although this approach removes the need for any non-visual sensors, it can also be coupled with an Inertial Measurement Unit.     

Inertial Sensed Ego‐motion for 3D Vision

Inertial sensors attached to a camera can provide valuable data about camera pose and movement. In biological vision systems, inertial cues provided by the vestibular system are fused with vision at an early processing stage. In this article we set a framework for the combination of these two sensing modalities. Cameras can be seen as ray direction measuring devices, and in the case of stereo vision, depth along the ray can also be computed. The ego‐motion can be sensed by the inertial sensors, but there are limitations determined by the sensor noise level. Keeping track of the vertical direction is required, so that gravity acceleration can be compensated for, and provides a valuable spatial reference. Results are shown of stereo depth map alignment using the vertical reference. The depth map points are mapped to a vertically aligned world frame of reference. In order to detect the ground plane, a histogram is performed for the different heights. Taking the ground plane as a reference plane for the acquired maps, the fusion of multiple maps reduces to a 2D translation and rotation problem. The dynamic inertial cues can be used as a first approximation for this transformation, allowing a fast depth map registration method. They also provide an image independent location of the image focus of expansion and center of rotation useful during visual based navigation tasks. © 2004 Wiley Periodicals, Inc.     

基于地标的轻量化无人机精准降落算法

轻量化无人机的机载计算机通常计算性能较弱,很难满足无人机实现精准降落的需要。针对这一问题,提出了一种基于地标的轻量化精准降落算法,通过识别对比颜色和指定形状实现快速实时地检测着陆标识,图像处理流程简单快速且准确,通过相对位置计算在二维层面得到无人机对于降落地标的相对位置和方向,引导无人机精准降落,算法执行过程不需要考虑相机焦距。实际测试结果表明,在一些特定如无人机自主充电的应用场景,该算法过程简单,稳定性和降落精度相较于传统方法较高。     

An adaptive observer framework for accurate feature depth estimation using an uncalibrated monocular camera

This paper presents a novel solution to the problem of depth estimation using a monocular camera undergoing known motion. Such problems arise in machine vision where the position of an object moving in three-dimensional space has to be identified by tracking motion of its projected feature on the two-dimensional image plane. The camera is assumed to be uncalibrated, and an adaptive observer yielding asymptotic estimates of focal length and feature depth is developed that precludes prior knowledge of scene geometry and is simpler than alternative designs. Experimental results using real camera imagery are obtained with the current scheme as well as the extended Kalman filter, and performance of the proposed observer is shown to be better than the extended Kalman filter-based framework.