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 <1.2 cm for the horizontal and vertical directions. The absolute accuracy is determined using a point-based evaluation, which results in the RMSE being <2 cm for the horizontal direction and <4 cm for the vertical direction, compared to the TLS reference. The results are consistent for the two different study areas with similar evaluation approaches but different flight planning and processing. In addition, the influence of different Global Navigation Satellite System (GNSS) master stations is investigated and no significant difference was found between Virtual Reference Stations (VRS) and a dedicated master station. Furthermore, to control the orientation of the point cloud, a parameter-based analysis using planes in object space was performed, which showed a good agreement with the reference within the noise level of the point cloud. The calculated quality parameters are all smaller than the manufacturer’s specifications and can be transferred to other multi-sensor systems.

The use of unmanned aerial vehicles (UAV) in monitoring applications is constantly increasing due to the improvement in sensor technology and the associated higher accuracy that can be achieved. As a result, UAV-based laser scanning is already being used in various deformation monitoring applications such as the monitoring of landslides or land deformations. The main challenges, which also limit the accuracy of the resulting georeferenced point cloud are given by the trajectory estimation, the measurement environment and the flight planning. Difficult conditions and high accuracy demands are especially given for the monitoring of a water dam. While the use of area-based measurements such as terrestrial laser scanning (TLS) is an already established approach for such monitoring tasks, the use of a similar technology on a platform such as a UAV is promising and investigated in this study by acquiring a single measurement epoch at a water dam. In addition to the proposal of a flight pattern for the measurements, the trajectory estimation results are evaluated in detail. Due to critical GNSS conditions, positioning errors lead to systematic shifts between single flight strips. Subsequent optimization with known control points allows the point cloud to be compared to a TLS reference. The difference between the two is shown to have a mean difference of 5 mm with a 9.2 mm standard deviation. This can be considered a highly promising result, especially as the potential for further improvement by using additional targets and sensors (e.g. camera) has been identified.

Many applications today require the precise determination of the position and orientation of a moving platform over time. However, especially in safety-critical areas, it is also important to derive quality characteristics of the trajectory estimation. This allows verification that sensors are operating within the precision and accuracy required for the application. In this paper, we propose a methodology for trajectory evaluation and address the challenges involved. Our approach is based on repeated measurements obtained using a closed loop rail track and allows the evaluation of the trajectory estimation in terms of precision and accuracy. Starting with the chronologically ordered raw data, the methodology first spatially sorts the measurements and then approximates them to a mean trajectory. The deviations between the single pose observations and the mean trajectory indicate the precision of the observed poses. With the addition of a higher-order reference, our methodology also determines the accuracy of the system under test. The applicability of our method is demonstrated by an exemplary evaluation of a low-cost inertial navigation system.