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ABSTRACT:Recent technological advancements have made active imaging sensors popular for 3D modelling and motion tracking. The 3D coordinates of signalised targets are traditionally estimated by matching conjugate points in overlapping images. Current 3D cameras can acquire point clouds at video frame rates from a single exposure station. In the area of 3D cameras, Microsoft and PrimeSense have collaborated and developed an active 3D camera based on the triangulation principle, known as the Kinect system. This off-the-shelf system costs less than $150 USD and has drawn a lot of attention from the robotics, computer vision, and photogrammetry disciplines. In this paper, the prospect of using the Kinect system for precise engineering applications was evaluated. The geometric quality of the Kinect system as a function of the scene (i.e. variation of depth, ambient light conditions, incidence angle, and object reflectivity) and the sensor (i.e. warm-up time and distance averaging) were analysed quantitatively. This system's potential in human body measurements was tested against a laser scanner and 3D range camera. A new calibration model for simultaneously determining the exterior orientation parameters, interior orientation parameters, boresight angles, leverarm, and object space features parameters was developed and the effectiveness of this calibration approach was explored.

Abstract. Deformation monitoring has been carried out in two epochs on Turtle Mountain, Alberta, using a high-precision total station and a terrestrial laser scanner. From the total station observations, coordinates have been computed for seven signalized target points in a least-squares network adjustment. Then, a deformation analysis using a MultiParameter Transformation has been performed to derive movements between epochs. The two point clouds obtained with the laser scanner were registered using the iterative closest point algorithm. Differences in elevation between the two point clouds were then derived for the entire scene. Results indicate a downward movement of South Peak, and no significant horizontal deformations were found.

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This paper investigates the problem of variance‐covariance modeling of atmospheric errors for the purposes of improving the accuracy of positioning using global navigation satellite systems, as well as improving estimates of the accuracy of such positioning. A method of modeling all variances and cross‐correlations among observations made in a network of positioning receivers is presented. This is done by generating a theoretical model of covariance behavior based on the physical nature of tropospheric and ionospheric errors, respectively, and then using data observed at a network of reference receivers to derive key parameters of the theoretical model. The applicability of this method is demonstrated for a network of 10 receivers with a total extent of 250 km. It is shown that proper modeling of covariances improves positioning accuracy an average of 22 percent.

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Terrestrial laser scanners are high-accuracy 3D imaging instruments that are capable of measuring deformations with sub-millimetre level accuracy in most closerange applications. Traditionally, deformation monitoring via laser scanning is performed by measuring distinct signalised targets. In this case, the centroid of these targets must be determined with great accuracy for optimum detectability. To achieve this, a least-squares target centroid extraction algorithm suitable for planar checkerboard-type targets is proposed for irregularly organised laser scanner data. These target centroids are then used in a free-station network adjustment for performing deformation analysis with no a priori assumptions about the deformation pattern. To ensure the optimum measurement accuracy, all systematic errors inherent to the instrument at the time of data acquisition needs to be removed. One of the methods for reducing these systematic errors is by performing selfcalibration of terrestrial laser scanners. In this paper, this was performed on-site to model the systematic errors of the scanner. It is demonstrated that the accuracy of the recovered translational movements were improved by an order of magnitude from the millimetre level to the sub-millimetre level using this approach. Despite the success of using laser scanners with signalised targets in deformation analysis, the main benefit of active sensors like terrestrial laser scanning systems is their ability to capture 3D information of the entire scene without installing markers. A new markerless deformation analysis technique that utilises intersection points derived from planar-features is proposed and tested in this paper. The extraction and intersection of planes in each point cloud can be performed semi-automatically or automatically. This new method is based on free-stationing and does not require a priori knowledge about stable control points or movement patterns. It can detect and measure both translational and rotational movements of the planes with minimum human interaction. The effectiveness of the proposed methodology is studied through simulations and real datasets captured with a phase-based Leica HDS6100 scanner.