智能空中三角测量中若干关键技术的研究
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摘要
空中三角测量是摄影测量中最关键的步骤,也是在摄影测量近百年的发展历程中,不同时期的研究重点,直至本世纪初仍是摄影测量工作者关注的焦点问题之一。在空中三角测量研究历史进程中,主要解决了高精度像片外方位元素的解算,包括附加参数的光束法区域网平差、GPS/IMU等辅助数据的联合平差,以及大规模稀疏矩阵的高效解算方法和测量误差自动评估与粗差剔除等可靠性理论方法。
随着计算机以及相关领域技术的发展,摄影测量步入数字摄影测量阶段,空中三角测量也进入了全数字自动空中三角测量阶段,主要研究全自动、高精度的像片连接点自动量测技术,其中Ackermann教授提出的最小二乘影像匹配方法有效地提高了自动影像匹配中单点量测精度。伴随着GPS/IMU技术在摄影测量中的应用,GPS/IMU引导的多片影像匹配和实时光束法区域网平差相结合的实施方案,解决了全自动空中三角测量中影像的初始拓扑关系,突破了传统自动空中三角测量先量测后平差的传统思路。反之,摄影区域中的影像没有GPS/IMU等辅助数据时,实现全自动的空中三角测量仍具有挑战性。另外,控制点的自动量测方法以及内外业相结合的控制点自动布设仍是全自动空中三角测量新的研究的方向。
随着低空无人机等简陋摄影平台在摄影测量中的广泛应用,打破了传统摄影测量的摄影模式。由于条件的限制,传统的摄影条件和严格的摄影要求,在简陋摄影平台下往往不能得到充分的满足。这些简陋摄影平台通常不具备IMU甚至GPS等辅助设备,航带内影像间出现大旋角、大倾角、航带间影像的影像重叠不能满足要求等非正常情况时有发生。正因为这些非正常的摄影情况出现,给本已相对成熟的全自动空中三角测量技术提出了新的研究课题,即如何在复杂的区域影像关系中,智能化地实现高精度的像片连接点自动量测,以及区域网光束法平差,也是本文研究意义所在。提出了智能空中三角测量的概念,旨在借鉴计算机中人工智能技术,将计算机与人有机地结合在一起,发挥电脑快速计算和快速记忆的特点,在人脑的控制下促进电脑的智能化,并引导人共同完成更加复杂的任务,如水域地区、荒漠地区等困难地区和复杂影像关系的空中三角测量问题。
为了解决复杂影像关系下的高精度的像片连接点自动量测问题,本文在智能化空中三角测量概念的基础上探讨了以多视立体模型为基本单元的高精度像点自动量测、粗差剔除与实时影像间几何关系的智能化估算,逐步精化的理论和方法。并通过多视立体模型的连接与合并,完成大区域的整体光束法平差,获得整个区域中影像的外方位元素、连接点的地面坐标以及相关改正参数,并进行整个区域的精度评定、弱区分析,提出供后续处理建议和控制点布设方案。因此本文研究基于GPS/IMU快速多视立体模型的建立方法；基于特征的影像匹配的多视立体模型建立方法；基于多视立体模型的多片最小二乘影像匹配方法；多视立体模型的局域网光束法自由网平差和粗差剔除等；主要研究内容如下：
(一)全自动多视立体模型影像间初始拓扑关系的建立
传统的空中三角测量数据处理过程中,影像间的初始拓扑关系通常是通过人工方式,根据测区飞行计划和获得影像情况人工建立的,一般按航带建立航带影像列表和航带关系列表,并以测区工程文件形式表达。航带内影像间通过链表构成具有60%重叠度的影像序列,每条航带间则通过上下航带间影像的偏移描述测区影像间的初始关系。这种人工方式建立影像间初始拓扑关系的方法,主要存在两大问题：一是不能很好表达航带间影像的一一对应关系,二是人工工作量大,是实现自动化的主要瓶颈。本文根据当前摄影测量中数码影像的特点,首次提出利用双视立体影像对的重叠方向(即左右视差的方向),自动确定摄影机安装方位,建立影像坐标系与摄影测量像片平面坐标系的关系,实现像点观测值的镜头系统误差正确改正。进而通过研究计算机图形学和图论的相关理论,充分利用现代摄影测量中获取GPS/IMU数据,全自动建立影像间的初始拓扑关系；对于无GPS/IMU等辅助导航数据的影像,则通过研究基于特征的影像匹配技术,利用特征匹配方法实现两两影像匹配,寻找当前影像的8邻域内的相邻影像,解算影像间的几何关系,完成测区影像间初始关系的自动建立,从而引导和构建多视立体模型下的多片影像匹配,实现全自动连接点自动量测和控制点点位分析。
(二)智能化误匹配点与粗差点自动剔除方法研究
误匹配点与粗差点是影像匹配中不可避免的产物,如何有效地利用稳健的几何模型估算方法自动地剔除误匹配点是本文研究的重要方面。主要通过两种经典的粗差剔除理论,对误匹配点进行剔除。
RANSAC随机采样推估模型是在空间后方交会解算中剔除具有粗差的控制点时提出解算模型,按摄影测量的基本原理,两两像片间的影像匹配点应满足共面条件,按RANSAC的基本原理随机取样进行共面条件的参数解算,并对同名点的残差进行分析,自动剔除误匹配点。对于平坦地区的影像,采用仿射变换模型,利用RANSAC算法确立同名像点的对应关系,自动剔除误匹配点。
对于多视立体模型的多片影像匹配中产生的误匹配点,应用DataSnoop粗差剔除理论,采用分权迭代的方法以及共面条件方程,构建基于多视立体模型的小区域光束法自由网平差。再采用严格的共面条件方程和最小二乘平差原理,进行更进一步的粗差检测与定位算法的研究。
(三)高精度像点量测与分区光束法自由网平差和整体平差
高精度的像点量测是提高区域网光束法平差精度的关键,反之精确的像片外方位元素可以提高影像匹配的精度和最小二乘影像匹配的可靠性。因此,研究基于高精度的影像匹配与区域网光束法平差是智能空中三角测量的关键技术。
本文采用多视立体模型作为最小高精度像点量测与自由网光束法平差区域,利用金字塔影像进行基于特征点的由粗到细的分频道匹配,并根据分层匹配结果完成自由网光束法平差,剔除粗差点的同时,精确估算每张影像的方位元素。最后在原始影像上,利用精确估算的影像的方位元素,采用基于共线条件的多片最小二乘影像匹配,完成高精度像点量测和分区光束法自由网平差。
将多视立体模型的匹配结果,按影像进行存储,按同名像点的分布进行弱区检查和最小平差条件检查同名点的观测次数,构建整体平差区域,进行光束法整体平差。
(四)控制点自动布设方案与控制点自动量测
根据误差理论,对光束法区域网平差的弱区进行分析,除了遵循一般布点原则和现有控制点布设规范,在弱区增加控制点布设,基于区域影像间的关系,分区自动预测控制点的位置。
按预测的控制点位进行特征点提取,寻找同名像点。采用基于核线约束的多片匹配方法,并对匹配点进行粗差探测处理；
控制点位输出成小影像和文本,并在移动设备(如笔记本电脑、PDA、IPad以及智能手机等)上进行显示,以指导外业人员在指定的位置上测量其控制点坐标。
Triangulation is one of the most important steps in photogrammetry. During more than100years, it is also a focusing problem in different development periods of photogrammetry. Until the beginning of this century, Triangulation is still one of research focuses that photogrammetry researchers are concerning. In the studying history process of aerial triangulation, it mainly solves the high precision solution of exterior orientation elements including the bundle block adjustment with additional parameters, the combined adjustment with GPS/IMU and other auxiliary data, high efficiency solution of large scale sparse matrix method and the method of reliability theory such as automatic measurement error evaluation and gross error detecting.
With the development of computer and related technology fields, photogrammetry enters the stage of digital photogrammetry and aerial triangulation also entered a full digital automatic aerial triangulation stage. It researches the full automatic and high precision measurement technique of the photo connection point in which Professor Ackermann proposed the least squares image matching method that can effectively improve the single point measurement accuracy of automatic image matching.
With the application of GPS/IMU technology in photography, it is a feasible solution of the automatic aerial triangulation to combine multi-image matching guided by GPS/IMU and real-time bundle block adjustment. This solution breaks through the old idea of traditional automatic aerial triangulation measurement that measurement is done before the adjustment. Otherwise, it is still a challenge to realize automatic aerial triangulation without GPS/IMU and other auxiliary data for the images in photography area. In addition, the method of automatic control point measurement and automatic control point layout combining the inside and outside the industry are still the research directions of full automatic aerial triangulation automatic.
With the wide application of crude photographic platform such as low-altitude UAVs in photogrammetry, the photographic mode of traditional photogrammetry is broken. Due to the condition limitation, the traditional photography condition and strict requirements of photography often cannot be fully satisfied. Some Irregular situations appear such as no GPS/IMU and other auxiliary data, the large rotation angle and large angle in the image of internal strip, and image overlap between strips beyond the requirements. Because of the appearance of the abnormal photography, new research problems are created in the already relatively mature automatic aerial triangulation, namely how to intelligently realize high precision automatic measurement of photograph connection point with the complex regional image relationship, and regional bundle adjustment. The significance of this paper is to propose the concept of intelligent aerial triangulation that hopes to achieve the combination of the brain and the computer, make full use the fast computation and fast memory of computer, and promote computer's intelligence and guide people to complete more complex tasks under the control of the human brain, such as aerial triangulation problem in water area, the desert area, difficult and complex image relationship.
In order to solve the high precision automatic measurement problem of image connection point under the complex image relationship, this paper proposed the intelligent aerial triangulation concept based theory and method that includes high precision automatic image point measurement with multi-view stereo model as the basic unit, error elimination and the refinement of the intelligent estimation of real-time imaging geometric relationship. And through the connection and merger of the multi-view stereo model, the whole region bundle adjustment can be completed. Exterior orientation elements of whole area images, the ground coordinates of the connection points and related correction parameters can be obtained. At the same time, the accuracy assessment and the weak area analysis of the whole area are done and follow-up treatment recommendations and control point layout scheme are provided in the form of the adjustment report. So this paper researches on fast multi-view stereo model based on GPS/IMU, fast multi-view stereo model based on feature image matching, multiple least squares image method based on multi-view stereo model, and region bundle adjustment of free network and error elimination of multi-view stereo model. The main research contents are as follows:
(1) Automatical establishment of the initial topological relation between images of a full automatic multi-view stereo model
In the traditional data process of aerial triangulation, the initial topology relationship between images is usually built manually according to the flight plan of survey area and the obtained images. Generally strip image list and strip relation list are established by the strip and expressed in the form of survey area project file. For images in a strip, the image sequence with60%overlapping is formed by the list. The initial relationship between survey area's images of different strip is described by the image offset of adjacent strip. For the artificial method of setting the initial topological relation between images, there are two main problems:one is that it can't express the corresponding relation of different strip images well. One is a large amount of manual work that is the main bottleneck of automation. According to the characteristics of digital image in photogrammetry, this paper first proposed to use the overlapping direction of a single digital stereo image pairs (left-right parallax direction) to determine automatically the installation position of the camera and create the coordinate relation between the image coordinate system and photogrammetry photograph plane system. At last, the lens system errors of image point observations can be properly corrected. Then through the study of computer graphics related theory and graph theory, topological relations between the initial images is established automatically based on the acquisition data of GPS/IMU in modern photography measurement. For the images without GPS/IMU aided navigation data, first realize the two-image matching using feature matching method through the research image matching technology based on feature. Then calculate geometrical relationship between adjacent images by searching the image of the8neighborhood. At last complete the automatic establishment of initial relationship between images, so as to guide and construct multi-image matching under the multi-view stereo model and realize automatic measurement of connection point and the analysis of control point position.
(2) Study on the automatic intelligent elimination method of error matching points and gross error points
Error matching points and coarse points are almost inevitable in image matching. The important aspect of this paper is how to effectively use the robust geometric model estimation method to automatically eliminate false matching points. Here are two kinds of classic gross error elimination theory for removing the error matching points.
RANSAC random sampling estimation model is put forward for removing control points with gross error in space resection. According to the basic principles of photogrammetry that image matching points between two photos should meet the coplanar condition, parameters of the coplanar condition can be calculated based on the principle of random sampling of the RANSAC. And the residual of the corresponding points is analyzed and error matching points can be automatically eliminated.For the images of the flat region, the affine transformation model is used to automatically eliminate the error matching points based on the correspondence relation of image corresponding point established by the RANSAC algorithm. For the error matching points produced in multi-view matching of multi-view stereo model. DataSnoop gross error elimination theory is used, and iterative process of weight functions and coplanar condition equations are used to establish free network bundle adjustment of small area based on multi-view stereo model. Study on the method of the error matching point elimination. Then the coplanarity condition equation and least squares adjustment principle are used to further study error detection and localization algorithm.
(3) The high precision image point measurement and partition bundle adjustment of free networks and overall adjustment
High precision image point measurement is the key to improve the accuracy of region bundle adjustment. Otherwise, the precise elements of exterior orientation can improve the accuracy of the image matching and the reliability of the least squares image matching. Therefore, it is the key technique of intelligent aerial triangulation to research the high precision image matching and region bundle adjustment.
This paper adopts multi-view stereo model as a minimal region of high precision image point measurement and free network bundle adjustment. Pyramid images are used for channel matching from coarse to fine scale based on feature point. And according to the hierarchical matching results, free network bundle adjustment can be completed. While gross error is eliminated, accurate estimation of each orientation element image is completed. Finally, in the original image, the high precision image point measurement and partition bundle adjustment of free network can be completed by using accurate estimation of orientation elements and multiple image least squares adjustment based on collinear condition.
The matching results of multi-view stereo model will be stored by image. Weak zone is checked based on the corresponding point distribution and the number of corresponding point observation is checked according to adjustment minimizing condition.
(4) Control point layout scheme and automatic control point measurement
According to the error theory, the weak area of bundle block adjustment is analyzed. In addition to the general principle of control point layout and the existing control point layout standard, the relationship between regional images is also considered to automatically predict the position of control points in increasing control points of weak area.
Feature points are extracted according to the control point prediction and the corresponding image points are found. The multi-image matching method based on epipolar constraint is used and the matching points are processed by gross error detection.
Control points are output into small images and text, and displayed in the mobile equipment (such as notebook computer, PDA, IPad and intelligent mobile phone etc) in order to guide the field workers measure the control point coordinate at the specified location.
随着计算机以及相关领域技术的发展,摄影测量步入数字摄影测量阶段,空中三角测量也进入了全数字自动空中三角测量阶段,主要研究全自动、高精度的像片连接点自动量测技术,其中Ackermann教授提出的最小二乘影像匹配方法有效地提高了自动影像匹配中单点量测精度。伴随着GPS/IMU技术在摄影测量中的应用,GPS/IMU引导的多片影像匹配和实时光束法区域网平差相结合的实施方案,解决了全自动空中三角测量中影像的初始拓扑关系,突破了传统自动空中三角测量先量测后平差的传统思路。反之,摄影区域中的影像没有GPS/IMU等辅助数据时,实现全自动的空中三角测量仍具有挑战性。另外,控制点的自动量测方法以及内外业相结合的控制点自动布设仍是全自动空中三角测量新的研究的方向。
随着低空无人机等简陋摄影平台在摄影测量中的广泛应用,打破了传统摄影测量的摄影模式。由于条件的限制,传统的摄影条件和严格的摄影要求,在简陋摄影平台下往往不能得到充分的满足。这些简陋摄影平台通常不具备IMU甚至GPS等辅助设备,航带内影像间出现大旋角、大倾角、航带间影像的影像重叠不能满足要求等非正常情况时有发生。正因为这些非正常的摄影情况出现,给本已相对成熟的全自动空中三角测量技术提出了新的研究课题,即如何在复杂的区域影像关系中,智能化地实现高精度的像片连接点自动量测,以及区域网光束法平差,也是本文研究意义所在。提出了智能空中三角测量的概念,旨在借鉴计算机中人工智能技术,将计算机与人有机地结合在一起,发挥电脑快速计算和快速记忆的特点,在人脑的控制下促进电脑的智能化,并引导人共同完成更加复杂的任务,如水域地区、荒漠地区等困难地区和复杂影像关系的空中三角测量问题。
为了解决复杂影像关系下的高精度的像片连接点自动量测问题,本文在智能化空中三角测量概念的基础上探讨了以多视立体模型为基本单元的高精度像点自动量测、粗差剔除与实时影像间几何关系的智能化估算,逐步精化的理论和方法。并通过多视立体模型的连接与合并,完成大区域的整体光束法平差,获得整个区域中影像的外方位元素、连接点的地面坐标以及相关改正参数,并进行整个区域的精度评定、弱区分析,提出供后续处理建议和控制点布设方案。因此本文研究基于GPS/IMU快速多视立体模型的建立方法；基于特征的影像匹配的多视立体模型建立方法；基于多视立体模型的多片最小二乘影像匹配方法；多视立体模型的局域网光束法自由网平差和粗差剔除等；主要研究内容如下：
(一)全自动多视立体模型影像间初始拓扑关系的建立
传统的空中三角测量数据处理过程中,影像间的初始拓扑关系通常是通过人工方式,根据测区飞行计划和获得影像情况人工建立的,一般按航带建立航带影像列表和航带关系列表,并以测区工程文件形式表达。航带内影像间通过链表构成具有60%重叠度的影像序列,每条航带间则通过上下航带间影像的偏移描述测区影像间的初始关系。这种人工方式建立影像间初始拓扑关系的方法,主要存在两大问题：一是不能很好表达航带间影像的一一对应关系,二是人工工作量大,是实现自动化的主要瓶颈。本文根据当前摄影测量中数码影像的特点,首次提出利用双视立体影像对的重叠方向(即左右视差的方向),自动确定摄影机安装方位,建立影像坐标系与摄影测量像片平面坐标系的关系,实现像点观测值的镜头系统误差正确改正。进而通过研究计算机图形学和图论的相关理论,充分利用现代摄影测量中获取GPS/IMU数据,全自动建立影像间的初始拓扑关系；对于无GPS/IMU等辅助导航数据的影像,则通过研究基于特征的影像匹配技术,利用特征匹配方法实现两两影像匹配,寻找当前影像的8邻域内的相邻影像,解算影像间的几何关系,完成测区影像间初始关系的自动建立,从而引导和构建多视立体模型下的多片影像匹配,实现全自动连接点自动量测和控制点点位分析。
(二)智能化误匹配点与粗差点自动剔除方法研究
误匹配点与粗差点是影像匹配中不可避免的产物,如何有效地利用稳健的几何模型估算方法自动地剔除误匹配点是本文研究的重要方面。主要通过两种经典的粗差剔除理论,对误匹配点进行剔除。
RANSAC随机采样推估模型是在空间后方交会解算中剔除具有粗差的控制点时提出解算模型,按摄影测量的基本原理,两两像片间的影像匹配点应满足共面条件,按RANSAC的基本原理随机取样进行共面条件的参数解算,并对同名点的残差进行分析,自动剔除误匹配点。对于平坦地区的影像,采用仿射变换模型,利用RANSAC算法确立同名像点的对应关系,自动剔除误匹配点。
对于多视立体模型的多片影像匹配中产生的误匹配点,应用DataSnoop粗差剔除理论,采用分权迭代的方法以及共面条件方程,构建基于多视立体模型的小区域光束法自由网平差。再采用严格的共面条件方程和最小二乘平差原理,进行更进一步的粗差检测与定位算法的研究。
(三)高精度像点量测与分区光束法自由网平差和整体平差
高精度的像点量测是提高区域网光束法平差精度的关键,反之精确的像片外方位元素可以提高影像匹配的精度和最小二乘影像匹配的可靠性。因此,研究基于高精度的影像匹配与区域网光束法平差是智能空中三角测量的关键技术。
本文采用多视立体模型作为最小高精度像点量测与自由网光束法平差区域,利用金字塔影像进行基于特征点的由粗到细的分频道匹配,并根据分层匹配结果完成自由网光束法平差,剔除粗差点的同时,精确估算每张影像的方位元素。最后在原始影像上,利用精确估算的影像的方位元素,采用基于共线条件的多片最小二乘影像匹配,完成高精度像点量测和分区光束法自由网平差。
将多视立体模型的匹配结果,按影像进行存储,按同名像点的分布进行弱区检查和最小平差条件检查同名点的观测次数,构建整体平差区域,进行光束法整体平差。
(四)控制点自动布设方案与控制点自动量测
根据误差理论,对光束法区域网平差的弱区进行分析,除了遵循一般布点原则和现有控制点布设规范,在弱区增加控制点布设,基于区域影像间的关系,分区自动预测控制点的位置。
按预测的控制点位进行特征点提取,寻找同名像点。采用基于核线约束的多片匹配方法,并对匹配点进行粗差探测处理；
控制点位输出成小影像和文本,并在移动设备(如笔记本电脑、PDA、IPad以及智能手机等)上进行显示,以指导外业人员在指定的位置上测量其控制点坐标。
Triangulation is one of the most important steps in photogrammetry. During more than100years, it is also a focusing problem in different development periods of photogrammetry. Until the beginning of this century, Triangulation is still one of research focuses that photogrammetry researchers are concerning. In the studying history process of aerial triangulation, it mainly solves the high precision solution of exterior orientation elements including the bundle block adjustment with additional parameters, the combined adjustment with GPS/IMU and other auxiliary data, high efficiency solution of large scale sparse matrix method and the method of reliability theory such as automatic measurement error evaluation and gross error detecting.
With the development of computer and related technology fields, photogrammetry enters the stage of digital photogrammetry and aerial triangulation also entered a full digital automatic aerial triangulation stage. It researches the full automatic and high precision measurement technique of the photo connection point in which Professor Ackermann proposed the least squares image matching method that can effectively improve the single point measurement accuracy of automatic image matching.
With the application of GPS/IMU technology in photography, it is a feasible solution of the automatic aerial triangulation to combine multi-image matching guided by GPS/IMU and real-time bundle block adjustment. This solution breaks through the old idea of traditional automatic aerial triangulation measurement that measurement is done before the adjustment. Otherwise, it is still a challenge to realize automatic aerial triangulation without GPS/IMU and other auxiliary data for the images in photography area. In addition, the method of automatic control point measurement and automatic control point layout combining the inside and outside the industry are still the research directions of full automatic aerial triangulation automatic.
With the wide application of crude photographic platform such as low-altitude UAVs in photogrammetry, the photographic mode of traditional photogrammetry is broken. Due to the condition limitation, the traditional photography condition and strict requirements of photography often cannot be fully satisfied. Some Irregular situations appear such as no GPS/IMU and other auxiliary data, the large rotation angle and large angle in the image of internal strip, and image overlap between strips beyond the requirements. Because of the appearance of the abnormal photography, new research problems are created in the already relatively mature automatic aerial triangulation, namely how to intelligently realize high precision automatic measurement of photograph connection point with the complex regional image relationship, and regional bundle adjustment. The significance of this paper is to propose the concept of intelligent aerial triangulation that hopes to achieve the combination of the brain and the computer, make full use the fast computation and fast memory of computer, and promote computer's intelligence and guide people to complete more complex tasks under the control of the human brain, such as aerial triangulation problem in water area, the desert area, difficult and complex image relationship.
In order to solve the high precision automatic measurement problem of image connection point under the complex image relationship, this paper proposed the intelligent aerial triangulation concept based theory and method that includes high precision automatic image point measurement with multi-view stereo model as the basic unit, error elimination and the refinement of the intelligent estimation of real-time imaging geometric relationship. And through the connection and merger of the multi-view stereo model, the whole region bundle adjustment can be completed. Exterior orientation elements of whole area images, the ground coordinates of the connection points and related correction parameters can be obtained. At the same time, the accuracy assessment and the weak area analysis of the whole area are done and follow-up treatment recommendations and control point layout scheme are provided in the form of the adjustment report. So this paper researches on fast multi-view stereo model based on GPS/IMU, fast multi-view stereo model based on feature image matching, multiple least squares image method based on multi-view stereo model, and region bundle adjustment of free network and error elimination of multi-view stereo model. The main research contents are as follows:
(1) Automatical establishment of the initial topological relation between images of a full automatic multi-view stereo model
In the traditional data process of aerial triangulation, the initial topology relationship between images is usually built manually according to the flight plan of survey area and the obtained images. Generally strip image list and strip relation list are established by the strip and expressed in the form of survey area project file. For images in a strip, the image sequence with60%overlapping is formed by the list. The initial relationship between survey area's images of different strip is described by the image offset of adjacent strip. For the artificial method of setting the initial topological relation between images, there are two main problems:one is that it can't express the corresponding relation of different strip images well. One is a large amount of manual work that is the main bottleneck of automation. According to the characteristics of digital image in photogrammetry, this paper first proposed to use the overlapping direction of a single digital stereo image pairs (left-right parallax direction) to determine automatically the installation position of the camera and create the coordinate relation between the image coordinate system and photogrammetry photograph plane system. At last, the lens system errors of image point observations can be properly corrected. Then through the study of computer graphics related theory and graph theory, topological relations between the initial images is established automatically based on the acquisition data of GPS/IMU in modern photography measurement. For the images without GPS/IMU aided navigation data, first realize the two-image matching using feature matching method through the research image matching technology based on feature. Then calculate geometrical relationship between adjacent images by searching the image of the8neighborhood. At last complete the automatic establishment of initial relationship between images, so as to guide and construct multi-image matching under the multi-view stereo model and realize automatic measurement of connection point and the analysis of control point position.
(2) Study on the automatic intelligent elimination method of error matching points and gross error points
Error matching points and coarse points are almost inevitable in image matching. The important aspect of this paper is how to effectively use the robust geometric model estimation method to automatically eliminate false matching points. Here are two kinds of classic gross error elimination theory for removing the error matching points.
RANSAC random sampling estimation model is put forward for removing control points with gross error in space resection. According to the basic principles of photogrammetry that image matching points between two photos should meet the coplanar condition, parameters of the coplanar condition can be calculated based on the principle of random sampling of the RANSAC. And the residual of the corresponding points is analyzed and error matching points can be automatically eliminated.For the images of the flat region, the affine transformation model is used to automatically eliminate the error matching points based on the correspondence relation of image corresponding point established by the RANSAC algorithm. For the error matching points produced in multi-view matching of multi-view stereo model. DataSnoop gross error elimination theory is used, and iterative process of weight functions and coplanar condition equations are used to establish free network bundle adjustment of small area based on multi-view stereo model. Study on the method of the error matching point elimination. Then the coplanarity condition equation and least squares adjustment principle are used to further study error detection and localization algorithm.
(3) The high precision image point measurement and partition bundle adjustment of free networks and overall adjustment
High precision image point measurement is the key to improve the accuracy of region bundle adjustment. Otherwise, the precise elements of exterior orientation can improve the accuracy of the image matching and the reliability of the least squares image matching. Therefore, it is the key technique of intelligent aerial triangulation to research the high precision image matching and region bundle adjustment.
This paper adopts multi-view stereo model as a minimal region of high precision image point measurement and free network bundle adjustment. Pyramid images are used for channel matching from coarse to fine scale based on feature point. And according to the hierarchical matching results, free network bundle adjustment can be completed. While gross error is eliminated, accurate estimation of each orientation element image is completed. Finally, in the original image, the high precision image point measurement and partition bundle adjustment of free network can be completed by using accurate estimation of orientation elements and multiple image least squares adjustment based on collinear condition.
The matching results of multi-view stereo model will be stored by image. Weak zone is checked based on the corresponding point distribution and the number of corresponding point observation is checked according to adjustment minimizing condition.
(4) Control point layout scheme and automatic control point measurement
According to the error theory, the weak area of bundle block adjustment is analyzed. In addition to the general principle of control point layout and the existing control point layout standard, the relationship between regional images is also considered to automatically predict the position of control points in increasing control points of weak area.
Feature points are extracted according to the control point prediction and the corresponding image points are found. The multi-image matching method based on epipolar constraint is used and the matching points are processed by gross error detection.
Control points are output into small images and text, and displayed in the mobile equipment (such as notebook computer, PDA, IPad and intelligent mobile phone etc) in order to guide the field workers measure the control point coordinate at the specified location.
引文
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