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提出了一种精确测定数码相机投影中心位置的方法,详细介绍了其工作原理和实施方法。利用Metro In经纬仪三坐标测量系统和Metro In-DPM数字工业摄影测量系统的高精度测量特点,提高了控制点坐标测量精度、控制点标志中心像点坐标量测精度并实现了相机的高精度标校。在一个试验里完成了高精度相机标校和投影中心位置精确测定,实现的投影中心位置测定精度优于5 mm。 相似文献
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在近景摄影测量中,为将所摄物体定向和检定照相机需要控制点。用两台经纬仪进行前方交会,以为所摄物体提供必须的控制点。在估算摄影测量的测定精度时,控制点的精度是异常重要的。文中对所导出的公式从数学和试验上进行了论证,以评定经纬仪位置与控制点位测定精度的关系并给出获得控制点最高精度的最佳经纬仪位置。 相似文献
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根据经纬仪测量系统的测量原理推算其空间任一点位的中误差计算公式,并进行必要的仿真分析。通过误差分布规律的仿真图像,分析经纬仪测量系统所测得的空间三维坐标点位的精度,从而有效地提高了经纬仪测量系统野外作业的测量精度。 相似文献
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在工业摄影测量中,影响测量精度的要素较多,为了提高摄影测量精度,在控制网型、被测物体、摄影距离等其它条件相同的情况下,采用不同的工业摄影测量相机,研究每个摄站不同旋转基线次数对精度的影响.通过对比不同旋转基线个数工况下的测量重复性,得出16个相机旋转基线比传统的2个相机旋转基线的测量重复性精度有明显提高;且随着相机旋转基线个数的增加,测量精度也会逐步提高. 相似文献
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在测距传感器不断轻量化、小型化以及室内外地图一体化导航应用的驱动下,三维(3D)室内移动测量成为当今研究和应用的热点,在室内建模、室内定位等新兴领域中的应用越来越广泛。3D室内移动测量系统通常配备激光扫描仪、全景相机、惯性测量单元(inertial measurement unit,IMU)系统和里程计等传感器,虽能实现3D室内点云数据的采集,但其距离传感器-激光扫描仪价格昂贵且便携性较差。彩色深度(RGB depth,RGB-D)相机为低成本3D室内移动测量系统构建提供了新的距离成像传感器选择,但主流型号RGB-D相机视场角小,继而导致数据采集效率远低于传统激光扫描仪,难以做到点云数据的完整覆盖与稳健采集,且易造成同时定位与制图(simultaneous localization and mapping,SLAM)过程中跟踪失败。针对以上问题,构建了一种低成本室内3D移动测量系统采集设备,通过组合多台消费级RGB-D相机构成大视场RGB-D相机阵列,提出了一种阵列RGB-D相机内外参数标定方法,并通过实验检验了设计系统采集的点云数据的精度。 相似文献
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引入灭点约束的TSAI两步法相机标定改进研究 总被引:1,自引:0,他引:1
针对TASI两步法相机标定过程无法求解相机主点位置的问题,引入灭点几何约束进行改进,推导并建立了平面格网上两组正交直线透视投影形成的双灭点与相机内参数以及TASI两步法相机标定过程参数间的严格数学关系,详细阐述了综合运用双灭点与径向准直约束计算相机内参数的迭代过程,并以此为基础,给出了相机外方位参数以及径向畸变参数的求解步骤。实验结果验证了该改进方法的有效性。 相似文献
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为满足摄影测量中相机检校的需要,选用一种室内相机检校场的精密测量方法与实验分析方法,对相机检校场进行检校。根据山东科技大学室内三维检校场中标志点的排列规则,点数众多的实际分布特点,选用高精度的经纬仪测量系统,选取最佳的仪器和基准尺摆放位置对检校场内的标志点进行测量。利用统计检验的方法对标志点点位的稳定性进行分析,判断出标志点稳定可以满足相机检校的需要。 相似文献
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测量机器人系统构成与精度研究 总被引:2,自引:0,他引:2
对测量机器人的特点进行了详细分析 ,对其自动化的工作原理进行了研究 ,其核心技术是用CCD摄像机获取目标图像 ,用计算机软件对数字图像进行分析和匹配 ,提取所需要的特征点 ,再配以精密马达伺服机构控制经纬仪系统的水平和垂直旋转 ,从而实现观测自动化。同时还简要介绍了与测量机器人配套使用的软件 ,并给出了试验结果。 相似文献
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基于多像灭点的相机定标 总被引:7,自引:3,他引:4
近年来,3维目标建模一直是计算机视觉及摄影测量领域的研究热门,其中拍摄目标的相机(或摄像机)定标是问题的关键之一.详细阐述了基于单像、利用灭点对相机进行定标的理论的特点及不足,并对其进行误差分析,从而提出了一种新的基于多方位、多像的灭点定标方法.这种方法克服了原有理论的缺陷,使标定出的相机内外方位参数更加精确、稳定. 相似文献
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J. Dold 《The Photogrammetric Record》1998,16(92):199-212
In the past few years the automation of digital industrial photogrammetric systems has increased dramatically. Due to digital image processing software, coded targets and automatic matching methods, a huge number of photogrammetric measurement tasks can be fully automated. In many cases a "one button click" is enough to provide the three dimensional co-ordinates of measured points without any manual interaction, immediately after acquiring the images. The technology of intelligent cameras is a logical step towards automated photogrammetric measurements. An intelligent camera, which has an integrated computer, analyses the image immediately after it is taken. This technology provides not only a much shorter processing time for the images but also more control over the measurement process just when it is needed, during image acquisition. This takes place in the form of real time feedback.
This paper describes the role of a digital intelligent camera in the automation of an industrial photogrammetric measurement system and gives an overview of existing automation techniques in industrial photogrammetry. As an example of an intelligent camera, the performance of the new INCA digital intelligent camera, developed and manufactured by Geodetic Services, Inc. (GSI) and distributed by Leica, is described. 相似文献
This paper describes the role of a digital intelligent camera in the automation of an industrial photogrammetric measurement system and gives an overview of existing automation techniques in industrial photogrammetry. As an example of an intelligent camera, the performance of the new INCA digital intelligent camera, developed and manufactured by Geodetic Services, Inc. (GSI) and distributed by Leica, is described. 相似文献
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A comparison of dense matching algorithms for scaled surface reconstruction using stereo camera rigs
Photogrammetric methods for dense 3D surface reconstruction are increasingly available to both professional and amateur users who have requirements that span a wide variety of applications. One of the key concerns in choosing an appropriate method is to understand the achievable accuracy and how choices made within the workflow can alter that outcome. In this paper we consider accuracy in two components: the ability to generate a correctly scaled 3D model; and the ability to automatically deliver a high quality data set that provides good agreement to a reference surface. The determination of scale information is particularly important, since a network of images usually only provides angle measurements and thus leads to unscaled geometry. A solution is the introduction of known distances in object space, such as base lines between camera stations or distances between control points. In order to avoid using known object distances, the method presented in this paper exploits a calibrated stereo camera utilizing the calibrated base line information from the camera pair as an observational based geometric constraint. The method provides distance information throughout the object volume by orbiting the object.In order to test the performance of this approach, four topical surface matching methods have been investigated to determine their ability to produce accurate, dense point clouds. The methods include two versions of Semi-Global Matching as well as MicMac and Patch-based Multi-View Stereo (PMVS). These methods are implemented on a set of stereo images captured from four carefully selected objects by using (1) an off-the-shelf low cost 3D camera and (2) a pair of Nikon D700 DSLR cameras rigidly mounted in close proximity to each other. Inter-comparisons demonstrate the subtle differences between each of these permutations. The point clouds are also compared to a dataset obtained with a Nikon MMD laser scanner. Finally, the established process of achieving accurate point clouds from images and known object space distances are compared with the presented strategies.Results from the matching demonstrate that if a good imaging network is provided, using a stereo camera and bundle adjustment with geometric constraints can effectively resolve the scale. Among the strategies for dense 3D reconstruction, using the presented method for solving the scale problem and PMVS on the images captured with two DSLR cameras resulted in a dense point cloud as accurate as the Nikon laser scanner dataset. 相似文献