Browsing by Author "Maalek, Reza"

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    Accuracy Assessment of UWB for Locating Resources on Construction Sites
    (2013-01-30) Maalek, Reza; Sadeghpour, Farnaz
    Continuous construction site monitoring and resource tracking can assist in ensuring workers’ safety, efficient materials handling and inventory tracking, timely use of resources, and theft prevention. Traditionally, on site data are collected manually, which is an error-prone, costly and time consuming task, particularly in large scale projects. Automated tracking can improve the management of construction projects through effective site monitoring. Based on the comparative evaluation of remote sensing technologies applicable to tracking conducted in this dissertation, Ultra-Wide Band (UWB) technology is shown to be efficient under dynamic characteristics and the harsh environment of construction sites. The use of UWB tracking systems for construction site applications has attracted the attention of researchers in recent years. However, to enable an effective application, it is important to establish the accuracy of its location estimation and its performance under suboptimal conditions of construction sites, which has not been systematically addressed in previous studies. Expanding on previous research, this dissertation presents a systematic approach towards assessing the accuracy of UWB Real-time Location Estimation System (RTLS) in both static and dynamic modes under conditions that can commonly occur on construction sites. A number of variables affecting the performance of the UWB technology in construction sites were identified that have not been addressed or conclusively treated previously; namely: the presence of metal objects that reflect UWB signals; best signals being blocked by other objects; tracking metal objects; removing the timing cables; tracking multiple tags simultaneously; decreased number of receivers; tracking resources with increasing velocities; tracking multiple tags to identify the orientation of the resource and tracking static and dynamic resources simultaneously. Series of experiments were conducted in which the impact of each condition was simulated by changing a variable. Each experiment was designed to reflect a scenario that commonly occurs on indoor construction sites. The experiments were carried out in two different environments, a laboratory and an active workshop. The accuracy and the relative error of location estimations were calculated for each experiment and the effects of influential variables were quantified. In this context, representative correlations were established between the accuracy and different influential variables. For all the experiments carried out here, more than 96% of the measurements in both 2D and 3D achieved an accuracy of less than 1 metre. On the basis of the results achieved in this work, recommendations have been provided for effective utilisation of the UWB technology in detecting objects and resources on construction sites. The findings of this research are essential to assess the feasibility of using UWB RTLS for different construction site applications, such as material handling and safety management.
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    Automated calibration of mobile cameras for 3D reconstruction of mechanical pipes
    (Wiley, 2021-04-06) Maalek, Reza; Lichti, Derek D
    This manuscript provides a new framework for calibration of optical instruments, in particular mobile cameras, using highly-redundant circular black and white target fields. New methods were introduced for (i) matching targets between images; (ii) adjusting the systematic eccentricity error of target centers; and (iii) iteratively improving the calibration solution through a free-network self-calibrating bundle adjustment. It was observed that the proposed target matching effectively matched circular targets in 270 mobile phone images, taken from a calibration laboratory, with robustness to Type II errors. The proposed eccentricity adjustment, which requires only camera projective matrices from two views, behaved synonymous to available closed-form solutions, which require several additional object space target information a priori. Finally, specifically for the case of the mobile devices, the calibration parameters obtained using our framework was found superior compared to in-situ calibration for estimating the 3D reconstructed radius of a mechanical pipe (approximately 45% improvement on average).
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    Development of an Automated Monitoring and Control System for Construction Projects
    (2017) Maalek, Reza; Ruwanpura, Janaka; Lichti, Derek; El-Sheimy, Naser; Jergeas, George; Fapojuwo, Abraham; Jaselskis, Edward
    Construction project progress monitoring and structural dimensional compliance control are essential for decision makers to identify discrepancies between the planned and the as-built states of a project, and take timely measures where required. In practice, monitoring is performed manually, a time consuming, error-prone and labour intensive task, particularly in large scale projects. Thus, large projects are monitored unsystematically by collecting limited onsite data, restricting the project management team to identify delays, and rework on time. The correct determination of the project’s performance also relies heavily on the correctness and completeness of the collected data during the monitoring process. Hence, site supervisory personnel spend considerable time just to manually control the quality of the manually collected onsite data. Several research studies have aimed to use remote sensing technologies such as LiDAR and cameras to acquire 3D point clouds of building elements to improve the quality of the collected data; however, these studies assume the planned BIM as a priori knowledge to assign the point clouds to their corresponding structural element, which provide inaccurate basis for reporting the as-built status of a project, especially when the planned and the actual differ or the planned model is not available with sufficient detail. Here, using the most up-to-date pattern recognition, robust statistical analysis and mathematical modelling techniques, a new robust approach was formulated, independent from a pre-existing planned model, to automatically generate the as-built model of common structural elements with predominantly planar and linear surfaces directly from the acquired point cloud. In the context of four experiments, ten sets of point clouds, nine from actual construction sites, were collected to express, verify and validate the diverse applicability of the proposed system for automated progress monitoring, structural displacement analysis, and dimensional conformation control. It was demonstrated that the novel robust planar and linear point cloud classification and segmentation method, presented here, achieved an overall accuracy of better than 90.4% for all datasets, indicating its generic applicability for construction projects. It was also shown that the proposed system is capable of automatically generating as-built models of common structural elements with the 3-5mm desired construction grade accuracy.
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    Modelling Extreme Wide-Angle Lens Cameras
    (Wiley, 2021-12-01) Lichti, Derek D; Tredoux, Wynand; Maalek, Reza; Helmholz, Petra; Radovanovic, Robert
    The use of consumer cameras fitted with extreme wide angle (EWA) lenses for photogrammetric measurement is increasing. Conventional modelling of EWA systems relies on the pinhole camera model and up to five radial lens distortion terms. Aiming to reduce model complexity, this paper reports an investigation into an alternate approach using fisheye lens models for EWA systems, despite them not falling strictly into to the fisheye category. Four fisheye models were tested on four different cameras under laboratory conditions. The self-calibration results show superior model fit for all fisheye models over the pinhole plus radial model in terms of residual RMS. The number radial distortion of terms required for the fisheye models was lower in all cases, so model complexity was reduced. Independent assessment revealed very similar 3D reconstruction accuracy for all models. The results suggest that fisheye modelling is an advantageous alternative for EWA lens systems.
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    New Confocal Hyperbola-based Ellipse Fitting with Applications to Estimating Parameters of Mechanical Pipes from Point Clouds
    (University of Calgary, 2021-03-14) Maalek, Reza; Lichti, Derek D
    This manuscript presents a new method for fitting ellipses to two-dimensional data using the confocal hyperbola approximation to the geometric distance of points to ellipses. The proposed method was evaluated and compared to established methods on simulated and real-world datasets. First, it was revealed that the confocal hyperbola distance considerably outperforms other distance approximations such as algebraic and Sampson. Next, the proposed ellipse fitting method was compared with five reliable and established methods proposed by Halir, Taubin, Kanatani, Ahn and Szpak. The performance of each method as a function of rotation, aspect ratio, noise, and arclength were examined. It was observed that the proposed ellipse fitting method achieved almost identical results (and in some cases better) than the gold standard geometric method of Ahn and outperformed the remaining methods in all simulation experiments. Finally, the proposed method outperformed the considered ellipse fitting methods in estimating the geometric parameters of cylindrical mechanical pipes from point clouds. The results of the experiments show that the confocal hyperbola is an excellent approximation to the true geometric distance and produces reliable and accurate ellipse fitting in practical settings.
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    Robust Detection of Non-overlapping Ellipses from Points with Applications to Circular Target Extraction in Images and Cylinder Detection in Point Clouds
    (Elsevier, 2021-04-14) Maalek, Reza; Lichti, Derek D
    Detection of non-overlapping ellipses from 2-dimensional (2D) edge points is an essential step towards solving typical photogrammetry problems pertaining to feature detection, calibration, and registration of optical instruments. For instance, circular and spherical black and white calibration and registration targets are represented as ellipses in images. Furthermore, the intersection of a cut plane with cylindrical point clouds generates 2D points following elliptic patterns. To this end, this study proposes a collection of new methods for the automatic and robust detection of non-overlapping ellipses from 2D points. These methods will first be applied to detect circular and spherical targets in images and, second, to detect cylinders in 3D point clouds. The method utilizes the Euclidian ellipticity and a new systematic and generalizable threshold to decide if a set of connected points follow an elliptic pattern. When connected points include outliers, the newly proposed robust Monte Carlo-based ellipse fitting method will be deployed. This method includes three new developments: (i) selecting initial subsamples using a bucketing strategy based on the polar angle of the points; (ii) detecting inlier points by reducing the robust ellipse fitting to a robust circle fitting problem; and (iii) choosing the best inlier set amongst all subsamples using adaptive, systematic, and generalizable selection criteria. A new process is presented to extract cylinders from a point cloud by detecting non-overlapping ellipses from the points projected onto an intersecting cut plane. The proposed methods were compared to established state-of-the-art methods, using simulated and real-world datasets, through the design of four sets of original experiments. The experiments include (i) comparisons of robust ellipse fitting; (ii) sensitivity analysis of the ellipse validation criteria; (iii) comparison of non-overlapping ellipse detection; and (iv) detection of pipes from terrestrial laser scanner point clouds. It was found that the proposed robust ellipse detection was superior to four reliable robust methods, including the popular least median of squares, in both simulated and real-world datasets. The proposed process for detecting non-overlapping ellipses achieved F-measure of 99.3% on real images, compared to 42.4%, 65.6%, and 59.2%, obtained using the methods of Fornaciari, Patraucean, and Panagiotakis, respectively. The proposed cylinder extraction method identified all detectable mechanical pipes in two real-world point clouds collected in laboratory and industrial construction site conditions. The results of this investigation show promise for the application of the proposed methods for automatic extraction of circular targets from images and pipes from point clouds.
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    Towards Automatic Digital Documentation and Progress Reporting of Mechanical Construction Pipes using Smartphones
    (Elsevier, 2021-04-27) Maalek, Reza; Lichti, Derek D; Maalek, Shahrokh
    This manuscript presents a new framework towards automated digital documentation and progress reporting of mechanical pipes in building construction projects, using smartphones. New methods were 13proposed to optimize video frame rate to achieve a desired image overlap; define metric scale for 3D reconstruction; extract pipes from point clouds; and classify pipes according to their planned bill of quantity radii. The effectiveness of the proposed methods in both laboratory (six pipes) and construction site (58 pipes) conditions was evaluated. It was observed that the proposed metric scale definition achieved sub-millimeter pipe radius estimation accuracy. Both laboratory and field experiments revealed that increasing the defined image overlap improved point cloud quality, pipe classification quality, and pipe radius/length estimation. Overall, it was found possible to achieve pipe classification F-measure, radius estimation accuracy, and length estimation percent error of 96.4%, 5.4mm, and 5.0%, respectively, on construction sites using at least 95% 21image overlap.