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ABSTRACT:The registration process of terrestrial laser scans (TLS) targets the problem of how to combine several laser scans in order to attain better information about features than what could be obtained through single scan. The main goal of the registration process is to estimate the parameters which determine geometrical variation between the origins of datasets collected from different locations. Scale, shifts, and rotation parameters are usually used to describe such variation. This paper presents a framework for the registration of overlapping terrestrial laser scans by establishing an automatic matching strategy that uses 3D linear features. More specifically, invariant separation characteristics between 3D linear features extracted from laser scans will be used to establish hypothesized conjugate linear features between the laser scans. These candidate matches are then used to geo-reference scans relative to a common reference frame. The registration workflow simulates the well-known RANndom Sample Consensus method (RANSAC) for determining the registration parameters, whereas the iterative closest projected point (ICPP) is utilized to determine the most probable solution of the transformation parameters from several solutions. The experimental results prove that the proposed methodology can be used for the automatic registration of terrestrial laser scans using linear features.

Commission I/Vb, ICWG I/Vb KEY WORDS: landslide dynamics, normal distance, bundle block adjustment with self-calibration, structure from motion, 3D dense surface reconstruction, Unmanned Aerial Vehicle (UAV) ABSTRACT:Landslides are among the major threats to urban landscape and manmade infrastructure. They often cause economic losses, property damages, and loss of lives. Temporal monitoring data of landslides from different epochs empowers the evaluation of landslide progression. Alignment of overlapping surfaces from two or more epochs is crucial for the proper analysis of landslide dynamics. The traditional methods for point-cloud-based landslide monitoring rely on using a variation of the Iterative Closest Point (ICP) registration procedure to align any reconstructed surfaces from different epochs to a common reference frame. However, sometimes the ICP-based registration can fail or may not provide sufficient accuracy. For example, point clouds from different epochs might fit to local minima due to lack of geometrical variability within the data. Also, manual interaction is required to exclude any non-stable areas from the registration process. In this paper, a robust image-based registration method is introduced for the simultaneous evaluation of all registration parameters. This includes the Interior Orientation Parameters (IOPs) of the camera and the Exterior Orientation Parameters (EOPs) of the involved images from all available observation epochs via a bundle block adjustment with selfcalibration. Next, a semi-global dense matching technique is implemented to generate dense 3D point clouds for each epoch using the images captured in a particular epoch separately. The normal distances between any two consecutive point clouds can then be readily computed, because the point clouds are already effectively co-registered. A low-cost DJI Phantom II Unmanned Aerial Vehicle (UAV) was customised and used in this research for temporal data collection over an active soil creep area in Lethbridge, Alberta, Canada. The customisation included adding a GPS logger and a Large-Field-Of-View (LFOV) action camera which facilitated capturing high-resolution geo-tagged images in two epochs over the period of one year (i.e., May 2014 and May 2015). Note that due to the coarse accuracy of the on-board GPS receiver (e.g., +/-5-10 m) the geo-tagged positions of the images were only used as initial values in the bundle block adjustment. Normal distances, signifying detected changes, varying from 20 cm to 4 m were identified between the two epochs. The accuracy of the co-registered surfaces was estimated by comparing non-active patches within the monitored area of interest. Since these non-active sub-areas are stationary, the computed normal distances should theoretically be close to zero. The quality control of the registration results showed that the average normal distance was approximately 4 cm, which is within the noise level of the reconstructed surfaces.

The so-called seam line discontinuity is a phenomenon that can be observed in point clouds captured with some panoramic terrestrial laser scanners. It is an angular discontinuity that is most apparent where the lower limit of the instrument's angular field-of-view intersects the ground. It appears as step discontinuities at the start (0° horizontal direction) and end (180°) of scanning. To the authors' best knowledge, its cause and its impact, if any, on point cloud accuracy have not yet been reported. This paper presents the results of a rigorous investigation into several hypothesized causes of this phenomenon: differences between the lower and upper elevation angle scanning limits; the presence of a vertical circle index error; and changes in levelling during scanning. New models for the angular observations have been developed and simulations were performed to independently study the impact of each hypothesized cause and to guide the analyses of real datasets. In order to scrutinize each of the hypothesized causes, experiments were conducted with seven real datasets captured with six different instruments: one hybrid-architecture scanner and five panoramic scanners, one of which was also operated as a hybrid instrument. This study concludes that the difference between the elevation angle scanning limits is the source of the seam line discontinuity phenomenon. Accuracy assessment experiments over real data captured in an indoor test facility demonstrate that the seam line discontinuity has no metric impact on the point clouds.

ABSTRACT:High-speed dual fluoroscopy (DF) imaging provides a novel, in-vivo solution to quantify the six-degree-of-freedom skeletal kinematics of humans and animals with sub-millimetre accuracy and high temporal resolution. A rigorous geometric calibration of DF system parameters is essential to ensure precise bony rotation and translation measurements. One way to achieve the system calibration is by performing a bundle adjustment with self-calibration. A first-time bundle adjustment-based system calibration was recently achieved. The system calibration through the bundle adjustment has been shown to be robust, precise, and straightforward. Nevertheless, due to the inherent absence of colour/semantic information in DF images, a significant amount of user input is needed to prepare the image observations for the bundle adjustment. This paper introduces a semi-automated methodology to minimise the amount of user input required to process calibration images and henceforth to facilitate the calibration task. The methodology is optimized for processing images acquired over a custom-made calibration frame with radio-opaque spherical targets. Canny edge detection is used to find distinct structural components of the calibration images. Edge-linking is applied to cluster the edge pixels into unique groups. Principal components analysis is utilized to automatically detect the calibration targets from the groups and to filter out possible outliers. Ellipse fitting is utilized to achieve the spatial measurements as well as to perform quality analysis over the detected targets. Single photo resection is used together with a template matching procedure to establish the image-to-object point correspondence and to simplify target identification. The proposed methodology provided 56,254 identified-targets from 411 images that were used to run a second-time bundle adjustment-based DF system calibration. Compared to a previous fully manual procedure, the proposed methodology has significantly reduced the amount of user input needed for processing the calibration images. In addition, the bundle adjustment calibration has reported a 50% improvement in terms of image observation residuals.