Laser scanners provide a three-dimensional sampled representation of the surfaces of objects. The spatial resolution of the data is much higher than that of conventional surveying methods. The data collected from different locations of a laser scanner must be transformed into a common coordinate system. If good a priori alignment is provided and the point clouds share a large overlapping region, existing registration methods, such as the Iterative Closest Point (ICP) or Chen and Medioni’s method, work well. In practical applications of laser scanners, partially overlapping and unorganised point clouds are provided without good initial alignment. In these cases, the existing registration methods are not appropriate since it becomes very difficult to find the correspondence of the point clouds. A registration method, the Geometric Primitive ICP with the RANSAC (GPICPR), using geometric primitives, neighbourhood search, the positional uncertainty of laser scanners, and an outlier removal procedure is proposed in this thesis. The change of geometric curvature and approximate normal vector of the surface formed by a point and its neighbourhood are used for selecting the possible correspondences of point clouds. In addition, an explicit expression of the position uncertainty of measurement by laser scanners is presented in this dissertation and this position uncertainty is utilised to estimate the precision and accuracy of the estimated relative transformation parameters between point clouds. The GP-ICPR was tested with both simulated data and datasets from close range and terrestrial laser scanners in terms of its precision, accuracy, and convergence region. It was shown that the GP-ICPR improved the precision of the estimated relative transformation parameters as much as a factor of 5. / In addition, the rotational convergence region of the GP-ICPR on the order of 10°, which is much larger than the ICP or its variants, provides a window of opportunity to utilise this automated registration method in practical applications such as terrestrial surveying and deformation monitoring.

Robotics and Automation field is booming in today´s scenario. Researchers and Technologist comes up with new ideas in the robotics field to achieve a higher productivity, flexibility and efficiency. To achieve the above goals, it shall be required that human and robot share their work space with each other and works in a collaborative nature. Safety is a main concern and in focus. Robot should not injure the operator in any way during working in robotic cell. In this master thesis main focus is to create a various test plans and validate them to ensure the safety level in robotic cell. The test plan should be validated in a real robot environment. The test plans consist of functional and individual verification of safety devices which are being used in a robotic cell at PTC which is known as smart automation lab. Apart from that it includes design simulation of robotic cells with manikins to ensure validation of safety in virtual environment. Design simulation of robotic cell with manikins are created in RobotStudio 6.06. However, smart components, trap routines, SafeMove and offline program in RAPID have been created. Various test results are incorporated in the results section to ensure the verification and validation of safe human robot collaboration of virtual environment in RobotStudio 6.06.

In recent years, many researchers have investigated automated progress tracking for construction projects. These efforts range from 2D photo-feature extraction to 3D laser scanners and radio frequency identification (RFID) tags. A multi-sensor data fusion model that utilizes multiple sources of information would provide a better alternative than a single-source model for tracking project progress. However, many existing fusion models are based on data fusion at the sensor and object levels and are therefore incapable of capturing critical information regarding a number of activities and processes on a construction site, particularly those related to non-structural trades such as welding, inspection, and installation activities. In this research, a workflow based data fusion framework is developed for construction progress, quality and productivity assessment. The developed model is based on tracking construction activities as well as objects, in contrast to the existing sensor-based models that are focussed on tracking objects. Data sources include high frequency automated technologies including 3D imaging and ultra-wide band (UWB) positioning. Foreman reports, schedule information, and other data sources are included as well. Data fusion and management process workflow implementation via a distributed computing network and archiving using a cloud-based architecture are both illustrated. Validation was achieved using a detailed laboratory experimental program as well as an extensive field implementation project. The field implementation was conducted using five months of data acquired on the University of Waterloo Engineering VI construction project, yielding promising results. The data fusion processes of this research provide more accurate and more reliable progress and earned value estimates for construction project activities, while the developed data management processes enable the secure sharing and management of construction research data with the construction industry stakeholders as well as with researchers from other institutions.

In recent years, many researchers have investigated automated progress tracking for construction projects. These efforts range from 2D photo-feature extraction to 3D laser scanners and radio frequency identification (RFID) tags. A multi-sensor data fusion model that utilizes multiple sources of information would provide a better alternative than a single-source model for tracking project progress. However, many existing fusion models are based on data fusion at the sensor and object levels and are therefore incapable of capturing critical information regarding a number of activities and processes on a construction site, particularly those related to non-structural trades such as welding, inspection, and installation activities. In this research, a workflow based data fusion framework is developed for construction progress, quality and productivity assessment. The developed model is based on tracking construction activities as well as objects, in contrast to the existing sensor-based models that are focussed on tracking objects. Data sources include high frequency automated technologies including 3D imaging and ultra-wide band (UWB) positioning. Foreman reports, schedule information, and other data sources are included as well. Data fusion and management process workflow implementation via a distributed computing network and archiving using a cloud-based architecture are both illustrated. Validation was achieved using a detailed laboratory experimental program as well as an extensive field implementation project. The field implementation was conducted using five months of data acquired on the University of Waterloo Engineering VI construction project, yielding promising results. The data fusion processes of this research provide more accurate and more reliable progress and earned value estimates for construction project activities, while the developed data management processes enable the secure sharing and management of construction research data with the construction industry stakeholders as well as with researchers from other institutions.

The volume of data acquired in production systems continues to expand. Emerging imaging technologies, such as machine vision systems (MVSs) and 3D surface scanners, diversify the types of data being collected, further pushing data collection beyond discrete dimensional data. These large and diverse datasets increase the challenge of extracting useful information. Unfortunately, industry still relies heavily on traditional quality methods that are limited to fault detection, which fails to consider important diagnostic information needed for process recovery. Modern measurement technologies should spur the transformation of statistical process control (SPC) to provide practitioners with additional diagnostic information. This dissertation focuses on how MVSs and 3D laser scanners can be further utilized to meet that goal. More specifically, this work: 1) reviews image-based control charts while highlighting their advantages and disadvantages; 2) integrates spatiotemporal methods with digital image processing to detect process faults and estimate their location, size, and time of occurrence; and 3) shows how point cloud data (3D laser scans) can be used to detect and locate unknown faults in complex geometries. Overall, the research goal is to create new quality control tools that utilize high density data available in manufacturing environments to generate knowledge that supports decision-making beyond just indicating the existence of a process issue. This allows industrial practitioners to have a rapid process recovery once a process issue has been detected, and consequently reduce the associated downtime. / Ph. D.

<p>Static terrestrial laser scanners have been increasingly used in three-dimensional data acquisition since it can rapidly provide accurate measurements with high resolution. Several scans from multiple viewpoints are necessary to achieve complete coverage of the surveyed objects due to occlusion and large object size. Therefore, in order to reconstruct three-dimensional models of the objects, the task of registration is required to transform several individual scans into a common reference frame. This thesis introduces three alternative approaches for the coarse registration of two adjacent scans, namely, feature-based approach, pseudo-conjugate point-based method, and closed-form solution. In the feature-based approach, linear and planar features in the overlapping area of adjacent scans are selected as registration primitives. The pseudo-conjugate point-based method utilizes non-corresponding points along common linear and planar features to estimate transformation parameters. The pseudo-conjugate point-based method is simpler than the feature-based approach since the partial derivatives are easier to compute. In the closed-form solution, a rotation matrix is first estimated by using a unit quaternion, which is a concise description of the rotation. Afterward, the translation parameters are estimated with non-corresponding points along the linear or planar features by using the pseudo-conjugate point-based method. Alternative approaches for fitting a line or plane to data with errors in three-dimensional space are investigated.</p><p><br></p><p>Experiments are conducted using simulated and real datasets to verify the effectiveness of the introduced registration procedures and feature fitting approaches. The proposed two approaches of line fitting are tested with simulated datasets. The results suggest that these two approaches can produce identical line parameters and variance-covariance matrix. The three registration approaches are tested with both simulated and real datasets. In the simulated datasets, all three registration approaches produced equivalent transformation parameters using linear or planar features. The comparison between the simulated linear and planar features shows that both features can produce equivalent registration results. In the real datasets, the three registration approaches using the linear or planar features also produced equivalent results. In addition, the results using real data indicates that the registration approaches using planar features produced better results than the approaches using linear features. The experiments show that the pseudo-conjugate point-based approach is easier to implement than the feature-based approach. The pseudo-conjugate point-based method and feature-based approach are nonlinear, so an initial guess of transformation parameters is required in these two approaches. Compared to the nonlinear approaches, the closed-form solution is linear and hence it can achieve the registration of two adjacent scans without the requirement of any initial guess for transformation parameters. Therefore, the pseudo-conjugate point-based method and closed-form solution are the preferred approaches for coarse registration using linear or planar features. In real practice, the planar features would have a better preference when compared to linear features since the linear features are derived indirectly by the intersection of neighboring planar features. To get enough lines with different orientations, planes that are far apart from each other have to be extrapolated to derive lines.</p><div><br></div>