Autonomous recovery of exterior orientation of imagery using free-form linear features
Pose estimation (space resection) is a prerequisite for a variety of tasks in fields such as photogrammetry, remote sensing, computer vision and scene analysis and so on. In traditional photogrammetry, the problem is solved by employing a minimum of three non-collinear 3-D to 2-D point correspondenc...
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ProQuest Dissertations & Theses
01.01.2002
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| Abstract | Pose estimation (space resection) is a prerequisite for a variety of tasks in fields such as photogrammetry, remote sensing, computer vision and scene analysis and so on. In traditional photogrammetry, the problem is solved by employing a minimum of three non-collinear 3-D to 2-D point correspondences in a least squares method based on the collinearity equations. Recent advances in computer technology require us to adopt more general methods, accommodating features other than points, especially linear features. During the past three years, a statistical approach—Modified Iterated Hough Transform (MINT) for simultaneously solving the parameter-estimation and matching problems has been developed in OSU digital photogrammetry group. This method has been successfully applied to a variety of photogrammetric application such as camera calibration, single photo resection, relative orientation, aerial triangulation, surface reconstruction and change detection by utilizing control straight lines and free-form linear features. However, the underlying principle in classical MINT is still point-based and employed in a point-wise way. In this work, a line-based approach as an extension to existing MINT system is proposed to autonomously recover the EOPs of frame imagery from 3-D free-form lines represented as ordered sequence of 3-D straight-line segments. There are several distinct characteristics in the proposed approaches. First, an optimal sequence for parameter estimation based on analytical investigation of the influences of EOPs on resulting deviation in image space is developed to avoid the computational explosion. Second, a scale space in parameter domain is constructed by recursively reducing the cell size of the accumulator array in a series of iterations, which largely facilitates the computing processes, while at the same time, provides accurate solutions. Third, the Iterative Closest Line segment algorithm is employed to effectively filter out the blunders and enhance its robustness. Experimental results using real data prove feasibility and robustness of the proposed method; as well as demonstrate its potential for a variety of applications in diverse fields, for example, applications integrating information coming from different sources, such as, change detection, surface reconstruction from laser and stereo, etc. |
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| AbstractList | Pose estimation (space resection) is a prerequisite for a variety of tasks in fields such as photogrammetry, remote sensing, computer vision and scene analysis and so on. In traditional photogrammetry, the problem is solved by employing a minimum of three non-collinear 3-D to 2-D point correspondences in a least squares method based on the collinearity equations. Recent advances in computer technology require us to adopt more general methods, accommodating features other than points, especially linear features. During the past three years, a statistical approach—Modified Iterated Hough Transform (MINT) for simultaneously solving the parameter-estimation and matching problems has been developed in OSU digital photogrammetry group. This method has been successfully applied to a variety of photogrammetric application such as camera calibration, single photo resection, relative orientation, aerial triangulation, surface reconstruction and change detection by utilizing control straight lines and free-form linear features. However, the underlying principle in classical MINT is still point-based and employed in a point-wise way. In this work, a line-based approach as an extension to existing MINT system is proposed to autonomously recover the EOPs of frame imagery from 3-D free-form lines represented as ordered sequence of 3-D straight-line segments. There are several distinct characteristics in the proposed approaches. First, an optimal sequence for parameter estimation based on analytical investigation of the influences of EOPs on resulting deviation in image space is developed to avoid the computational explosion. Second, a scale space in parameter domain is constructed by recursively reducing the cell size of the accumulator array in a series of iterations, which largely facilitates the computing processes, while at the same time, provides accurate solutions. Third, the Iterative Closest Line segment algorithm is employed to effectively filter out the blunders and enhance its robustness. Experimental results using real data prove feasibility and robustness of the proposed method; as well as demonstrate its potential for a variety of applications in diverse fields, for example, applications integrating information coming from different sources, such as, change detection, surface reconstruction from laser and stereo, etc. |
| Author | Lin, Hsiang Tseng |
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| Title | Autonomous recovery of exterior orientation of imagery using free-form linear features |
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