Abstract
Abstract. Laser scanner generated point cloud and photogrammetric imagery are complimentary data for many applications and services. Misalignment between imagery and point cloud data is a common problem, which causes to inaccurate products and procedures. In this paper, a novel strategy is proposed for coarse to fine registration between close-range imagery and terrestrial laser scanner point cloud data. In such a case, tie points are extracted and matched from photogrammetric imagery and preprocessing is applied on generated tie points to eliminate non-robust ones. At that time, for every tie point, two neighbor pixels are selected and matched in all overlapped images. After that, coarse interior orientation parameters (IOPs) and exterior orientation parameters (EOPs) of the images are employed to reconstruct object space points of the tie point and its two neighbor pixels. Then, corresponding nearest points to the object space photogrammetric points are estimated in the point cloud data. Estimated three point cloud points are used to calculate a plane and its normal vector. Theoretically, every object space tie point should be located on the aforementioned plane, which is used as conditional equation alongside the collinearity equation to fine register the photogrammetric imagery network. Attained root mean square error (RMSE) results on check points, have been shown less than 2.3 pixels, which shows the accuracy, completeness and robustness of the proposed method.
Highlights
Photogrammetry and computer vision are the main data providers for municipalities, mapping systems, urban services
Inaccurate calibration and estimation of the interior orientation parameters (IOPs) and exterior orientation parameters (EOPs) between imagery and point cloud datasets leads to failure of the fine registration and alignment
In such a case, proposed strategy using tie points of the photogrammetric imagery and converting it to object space points. These points should be as possible as close to the point cloud data if the IOPs and EOPs of the imagery network to be correct. By considering such an assumption, conditional equation beside to the collinearity equations, enforcing object space photogrammetric points to lie on the point cloud estimated differential planes to estimate correct IOPs and EOPs of the imagery data
Summary
Photogrammetry and computer vision are the main data providers for municipalities, mapping systems, urban services. Inaccurate calibration and estimation of the interior orientation parameters (IOPs) and exterior orientation parameters (EOPs) between imagery and point cloud datasets leads to failure of the fine registration and alignment To overcome such a misalignment variety of methods have been proposed, which can be categorized to groups as fallow. We are consuming non corresponding features [points, lines and planes] between photogrammetric imagery and point cloud datasets for coarse to fine registration. In such a case, proposed strategy using tie points of the photogrammetric imagery and converting it to object space points.
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