Generating Absolute-Scale Point Cloud Data of Built Infrastructure Scenes Using a Monocular Camera Setting

The global scale of Point Cloud Data (PCD) generated through monocular photo/videogrammetry is unknown, and can be calculated using at least one known dimension of the scene. Measuring one or more dimensions for this purpose induces a manual step in the 3D reconstruction process; this increases the effort and reduces the speed of reconstructing scenes, and induces substantial human error in the process due to the high level of measurement accuracy needed. Other ways of measuring such dimensions are based on acquiring additional information by either using extra sensors or specific classes of objects existing in the scene; we found that these solutions are not simple, cost effective or general enough to be considered practical for reconstructing both indoor and outdoor built infrastructure scenes. To address the issue, in this paper, we propose a novel method for automatically calculating the absolute scale of built infrastructure PCD. We use a pre-measured cube for outdoor scenes and a sheet of paper for indoor environments as the calibration patterns. Assuming that the dimensions of these objects are known, the proposed method extracts the objects’ corner points in 2D video frames using a novel algorithm. The extracted corner points are then matched between the consecutive frames. Finally, the corresponding corner points are reconstructed along with other features of the scenes to determine the real world scale. To evaluate the performance of the method, ten indoor and ten outdoor cases were selected and the absolute-scale PCD for each case was computed. Results illustrated the proposed algorithm is able to reconstruct the predefined objects with a high success rate while the generated absolute scale PCD is sufficiently accurate.This is the accepted manuscript. The final version is available from ASCE at http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.000041

Multistep Explicit Stereo Camera Calibration Approach to Improve Euclidean Accuracy of Large-Scale 3D Reconstruction

The spatial accuracy of point clouds generated by stereo image-based 3D reconstruction algorithms is very sensitive to the intrinsic and extrinsic camera parameters determined during camera calibration. The existing camera calibration algorithms induce a significant amount of error due to poor estimation accuracies in camera parameters when they are used for large-scale scenes such as mapping civil infrastructure. This leads to higher uncertainties in the location of 3D points, and may result in the failure of the whole reconstruction process. This paper proposes a novel procedure to address this problem. It hypothesizes that a set of multiple calibrations created by videotaping a moving calibration pattern along a specific path can increase overall calibration accuracy. This is achieved by using conventional camera calibration algorithms to perform separate estimations for some predefined distance values. The result, which includes multiple sets of camera parameters, is then used in the Structure from Motion process to improve the Euclidean accuracy of the reconstruction. The proposed method has been tested on infrastructure scenes and experimental analyses indicate more than 25% improvement in the spatial accuracy of 3D points.This is the accepted manuscript. The final version is available from ASCE at http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.000045

Predicting movements of onsite workers and mobile equipment for enhancing construction site safety

Tens of thousands of time-loss injuries and deaths are annually reported from the construction sector, and a high percentage of them are due to the workers being struck by mobile equipment on sites. In order to address this site safety issue, it is necessary to provide proactive warning systems. One critical part in such systems is to locate the current positions of onsite workers and mobile equipment and also predict their future positions to prevent immediate collisions. This paper proposes novel Kalman filters for predicting the movements of the workers and mobile equipment on the construction sites. The filters take the positions of the equipment and workers estimated from multiple video cameras as input, and output the corresponding predictions on their future positions. Moreover, the filters could adjust their predictions based on the worker or equipment's previous movements. The effectiveness of the filters has been tested with real site videos and the results show the high prediction accuracy of the filters

A cost-effective, mobile platform-based, photogrammetric approach for continuous structural deformation monitoring

PhD ThesisWith the evolution of construction techniques and materials technology, the design of modern civil engineering infrastructure has become increasingly advanced and complex. In parallel to this, the development and application of appropriate and efficient monitoring technologies has become essential. Improvement in the performance of structural monitoring systems, reduction of labour and total implementation costs have therefore become important issues that scientists and engineers are committed to solving. In this research, a non-intrusive structural monitoring system was developed based on close-range photogrammetric principles. This research aimed to combine the merits of photogrammetry and latest mobile phone technology to propose a cost-effective, compact (portable) and precise solution for structural monitoring applications. By combining the use of low-cost imaging devices (two or more mobile phone handsets) with in-house control software, a monitoring project can be undertaken within a relatively low budget when compared to conventional methods. The system uses programmable smart phones (Google Android v.2.2 OS) to replace conventional in-situ photogrammetric imaging stations. The developed software suite is able to control multiple handsets to continuously capture high-quality, synchronized image sequences for short or long-term structural monitoring purposes. The operations are fully automatic and the system can be remotely controlled, exempting the operator from having to attend the site, and thus saving considerable labour expense in long-term monitoring tasks. In order to prevent the system from crashing during a long-term monitoring scheme, an automatic system state monitoring program and a system recovery module were developed to enhance the stability. In considering that the image resolution for current mobile phone cameras is relatively low (in comparison to contemporary digital SLR cameras), a target detection algorithm was developed for the mobile platform that, when combined with dedicated target patterns, was found to improve the quality of photogrammetric target measurement. Comparing the photogrammetric results with physical measurements, which were measured using a Zeiss P3 analytical plotter, the returned accuracy achieved was 1/67,000. The feasibility of the system has been proven through the implementation of an indoor simulation test and an outdoor experiment. In terms of using this system for actual structural monitoring applications, the optimal relative accuracy of distance measurement was determined to be approximately 1/28,000 under laboratory conditions, and the outdoor experiment returned a relative accuracy of approximately 1/16,400

Development of Transformations between Designed and Built Structural Systems and Pipe Assemblies

Fabrication of steel assemblies is a challenging process using existing machines to perform the tasks involved such as cutting, drilling, and punching. Due to inaccuracies in the fabrication processes, imperfections will inevitably happen. In addition to the fabrication inaccuracies, errors may occur during transportation or due to the temperature changes on construction sites. These challenges become more important in the offsite construction as it requires sequenced fabrication, transportation and installation. Current approaches for quality inspection, in general, and discrepancy analysis, in particular, lack a sufficient level of automation and are prone to error due to the intensive manual work involved. Hence, a proactive framework is substantially required to systematically monitor the fabrication process and control the accuracy of assemblies in order to expedite the erection and installation processes. Additionally, finding defective assemblies is traditionally done through fitting trials on construction sites, which has always been a key challenge as it is associated with rework. Furthermore, realigning the defective assemblies is currently performed based on the workers’ experience and lacks automated planning. Therefore, detecting the defective parts in a timely manner and in a systematic way can expedite the erection process and avoids significant delays in construction projects and huge costs as a consequence. This research aims to improve the fabrication and installation processes by detecting the incurred inaccuracies automatically and plan for realignment of the defective components systematically. In summary, the required framework to achieve these objectives includes four primary steps: (1) Preprocessing and basic compliance checking, (2) Spatial discrepancy detection and characterization, (3) Calculation of the required alignments and adjustments, and (4) Generalization of the realignment planning and actuation strategy frameworks for parallel systems. The automated compliance checking and discrepancy analysis is performed employing advanced 3D imaging technologies which have recently opened up a wide range of solutions to acquire as-built status. Characterization of the detected discrepancies is performed by employing robotics forward kinematics concepts and combining with 3D imaging techniques. The required alignment is calculated accordingly using the robotic analogy and inverse kinematic concept. Although the proposed approach can be applied in any types of construction assembly, this thesis mainly focuses on industrial facilities such as steel pipe modules and pipe spools, in particular. Contributions of developing the described framework include: (1) Developing a proactive strategy for rework avoidance, (2) Algorithmic and programmable framework, (3) Efficiency and robustness of the functions and metrics developed, and (4) Time effectiveness of the framework

A Videogrammetric As-Built Data Collection Method for Digital Fabrication of Sheet Metal Roof Panels

A roofing contractor typically needs to acquire as-built dimensions of a roof structure several times over the course of its build to be able to digitally fabricate sheet metal roof panels. Obtaining these measurements using the exiting roof surveying methods could be costly in terms of equipment, labor, and/or worker exposure to safety hazards. This paper presents a video-based surveying technology as an alternative method which is simple to use, automated, less expensive, and safe. When using this method, the contractor collects video streams with a calibrated stereo camera set. Unique visual characteristics of scenes from a roof structure are then used in the processing step to automatically extract as-built dimensions of roof planes. These dimensions are finally represented in a XML format to be loaded into sheet metal folding and cutting machines. The proposed method has been tested for a roofing project and the preliminary results indicate its capabilities