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.
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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