Search

In almost all projects, in which terrestrial laser scanning is used, the scans must be registered after the data acquisition. Despite more and more new and automated methods for registration, the classical target-based registration is still one of the standard procedures. The advantages are obvious: independence from the scan object, the geometric configuration can often be influenced and registration results are easy to interpret. When plane black-and-white targets are used, the algorithm for estimating the target center fits a plane through the scan of a target, anyway. This information about the plane orientation has remained unused so far. Hence, including this information in the registration does not require any additional effort in the scanning process. In this paper, we extend the target-based registration by the plane orientation. We describe the required methodology, analyze the benefits in terms of precision and reliability and discuss in which cases the extension is useful and brings a relevant advantage. Based on simulations and two case studies we find out that especially for registrations with bad geometric configurations the extension brings a big advantage. The extension enables registrations that are much more precise. These are also visible on the registered point clouds. Thus, only a methodological change in the target-based registration improves its results.

The use of UAV-based laser scanning systems is increasing due to the rapid development in sensor technology, especially in applications such as topographic surveys or forestry. One advantage of these multi-sensor systems is the possibility of direct georeferencing of the derived 3D point clouds in a global reference frame without additional information from Ground Control Points (GCPs). This paper addresses the quality analysis of direct georeferencing of a UAV-based laser scanning system focusing on the absolute accuracy and precision of the system. The system investigated is based on the RIEGL miniVUX-SYS and the evaluation uses the estimated point clouds compared to a reference point cloud from Terrestrial Laser Scanning (TLS) for two different study areas. The precision is estimated by multiple repetitions of the same measurement and the use of artificial objects, such as targets and tables, resulting in a standard deviation of

The target-based point cloud registration and calibration of terrestrial laser scanners (TLSs) are mathematically modeled and solved by the least-squares adjustment. However, usual stochastic models are simplified to a large amount: They generally employ a single point measurement uncertainty based on the manufacturers’ specifications. This definition does not hold true for the target-based calibration and registration due to the fact that the target centroid is derived from multiple measurements and its uncertainty depends on the detection procedure as well. In this study, we empirically investigate the precision of the target centroid detection and define an empirical stochastic model in the form of look-up tables. Furthermore, we compare the usual stochastic model with the empirical stochastic model on several point cloud registration and TLS calibration experiments. There, we prove that the values of usual stochastic models are underestimated and incorrect, which can lead to multiple adverse effects such as biased results of the estimation procedures, a false a posteriori variance component analysis, false statistical testing, and false network design conclusions. In the end, we prove that some of the adverse effects can be mitigated by employing the a priori knowledge about the target centroid uncertainty behavior.

This article investigates the usage of terrestrial laser scanner (TLS) point clouds for monitoring the gradual movements of soil masses due to freeze–thaw activity and water saturation, commonly referred to as solifluction. Solifluction is a geomorphic process which is characteristic for hillslopes in (high-)mountain areas, primarily alpine periglacial areas and the arctic. The movement can reach millimetre-to-centimetre per year velocities, remaining well below the typical displacement mangitudes of other frequently monitored natural objects, such as landslides and glaciers. Hence, a better understanding of solifluction processes requires increased spatial and temporal resolution with relatively high measurement accuracy. To that end, we developed a workflow for TLS point cloud processing, providing a 3D vector field that can capture soil mass displacement due to solifluction with high fidelity. This is based on the common image-processing techniques of feature detection and tracking. The developed workflow is tested on a study area placed in Hohe Tauern range of the Austrian Alps with a prominent assemblage of solifluction lobes. The derived displacements were compared with the established geomonitoring approach with total station and signalized markers and point cloud deformation monitoring approaches. The comparison indicated that the achieved results were in the same accuracy range as the established methods, with an advantage of notably higher spatial resolution. This improvement allowed for new insights considering the solifluction processes.

Due to improved technology terrestrial laser scanners (TLS) are increasingly used for tasks demanding high accuracy, such as deformation monitoring. Within this field, often long-term measurements are acquired, for which the temporal stability of the laser scanner’s observations need to be assured or at least its magnitude and influence factors should be known. While these influence factors have been investigated for most of the geo-sensors taking part in long-term monitoring, it has not been investigated for TLS yet. In this study, we empirically reveal the stability of terrestrial laser scanner observations at long-term measurements. With these investigations, we can analyze the drifts, which occur in the polar observations during the warm-up phase and after it. It is shown that the drifts cause both rigid body movements and inner shape deformations of the point cloud. By re-stationing the scanner during long-term measurements, the drifts in the vertical angle in particular can be reduced by half.

During the registration and georeferencing of terrestrial laser scans, it is common to use targets to mark discrete points. To improve the accuracy of the registration, the uncertainties of the target center estimation (TCE) have to be minimized. The present study examines different factors influencing the precision of the TCE. Here, the focus is on the algorithm and the target design. It is determined that, in general, the uncertainties of the TCE are much smaller than those indicated by the manufacturers. By comparing different algorithms for the first time, it was possible to clearly determine that an algorithm using image correlations yields the smallest standard deviations for the TCE. A comparison of different target designs could not identify an ideal commercially available target. For this reason, a new target, the BOTA8 (BOnn TArget with 8-fold pattern) was developed, which leads to smaller standard deviations than the previous targets. By choosing the best algorithm and improving the target design, standard deviations of 0.5 mm in distance direction and 1.2 arcsec in angular direction for a scan distance up to 100 m were achieved with the laser scanner Leica ScanStation P20. The uncertainties could be reduced by several millimetres and angular seconds compared to the manufacturer’s targets and software.