Browsing by Author "Al-Durgham, Kaleel"

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    Explanation for the seam line discontinuity in terrestrial laser scanner point clouds
    (Elsevier, 2019-08) Lichti, Derek D.; Glennie, Craig L.; Al-Durgham, Kaleel; Jahraus, Adam; Steward, Jeremy
    The so‐called seam line discontinuity is a phenomenon that can be observed in point clouds captured with some panoramic terrestrial laser scanners. It is an angular discontinuity that is most apparent where the lower limit of the instrument’s angular field‐of‐view intersects the ground. It appears as step discontinuities at the start (0° horizontal direction) and end (180°) of scanning. To the authors’ best knowledge, its cause and its impact, if any, on point cloud accuracy have not yet been reported. This paper presents the results of a rigorous investigation into several hypothesized causes of this phenomenon: differences between the lower and upper elevation angle scanning limits; the presence of a vertical circle index error; and changes in levelling during scanning. New models for the angular observations have been developed and simulations were performed to independently study the impact of each hypothesized cause and to guide the analyses of real datasets. In order to scrutinize each of the hypothesized causes, experiments were conducted with seven real datasets captured with six different instruments: one hybrid‐architecture scanner and five panoramic scanners, one of which was also operated as a hybrid instrument. This study concludes that the difference between the elevation angle scanning limits is the source of the seam line discontinuity phenomenon. Accuracy assessment experiments over real data captured in an indoor test facility demonstrate that the seam line discontinuity has no metric impact on the point clouds.
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    Geometric Features Extraction and Automated Registration of Static Laser Scans Using Linear Features
    (2014-08-05) Al-Durgham, Kaleel; Habib, Ayman
    Laser scanning systems are employed in a wide variety of applications, such as digital building model generation, industrial site modeling, cultural heritage documentation, and other civilian and military needs. Depending on the mounting platform used, laser scanners can be classified into two groups: static or kinematic. A static laser scanner usually refers to a terrestrial laser scanner that is mounted on a tripod, while a kinematic laser scanner could be mounted on a fixed-wing plane, helicopter, or ground/terrestrial vehicle. A complete 3D model for a given site typically cannot be derived from a single scan. Therefore, several scans with significant overlap are needed to cover the entire site and also to obtain better information about the site than is possible from a single scan. When a static laser scanner is utilized to collect several scans to achieve complete coverage of a site of interest, the collected scans are referenced to different local frames that are associated with the individual scanner locations. Hence, a registration process must be performed in order to combine the several laser scans. The main goal of the registration process is to estimate the transformation parameters which determine the geometric variations between the reference frames of the collected datasets from different locations. The scale, shifts, and rotation parameters are generally used to describe such variations. The main objective of this thesis is a registration procedure for two overlapping static terrestrial laser scans using linear features that have been automatically extracted and matched in the available scans. The invariant characteristics of linear features in 3D space are utilized for establishing hypothesized matches of linear features between the overlapping scans; hence, the registration is performed in a pairwise scenario. Three alternative matching strategies are proposed to form correspondences between the linear features in overlapping laser scans. Another objective of this research is a segmentation/extraction procedure for low-level geometric features, such as planes,3D lines, and cylinders, from static and mobile laser scanning data, which is conducted through a region-growing segmentation process. The main characteristics of the proposed segmentation procedure are utilization of the proper parameterization models for the features of interest as well as consideration of the noise level and the point density variation within the laser scanning data. Experiments were performed to verify the outcome of the proposed segmentation and registration procedures. The segmentation experiments were conducted on static-terrestrial, mobile-terrestrial, and airborne laser scans, and an existing quality control procedure was used to evaluate the segmentation results. In the registration experiments, the proposed matching strategies were tested with real datasets of static laser scans captured over sites that are rich with linear features.