Direct Georeferencing Research Articles

IMPORTANCE OF PRECISE GRAVITY FIELD MODELING IN DIRECT GEOREFERENCING AND AERIAL PHOTOGRAMMETRY: A CASE STUDY FOR SWEDEN

Abstract. Direct georeferencing of airborne mobile mapping systems is developing with unprecedented speed using GNSS/INS integration. Removal of systematic errors is required for achieving a high accurate georeferenced product in mobile mapping platforms with integrated GNSS/INS sensors. It is crucial to consider the deflection of verticals (DOV) in direct georeferencing due to the recently improved INS sensor accuracy. This study determines the DOV using Sweden’s EGM2008 model and gravity data. The influence of the DOVs on horizontal and vertical coordinates and considering different flight heights is assessed. The results confirm that the calculated DOV components using the EGM2008 model are sufficiently accurate for aerial photogrammetry purposes except for the mountainous areas because the topographic signal is not modeled correctly.

HIGH-DEFINITION POINT CLOUD MAP-BASED 3D LiDAR-IMU CALIBRATION FOR SELF-DRIVING APPLICATIONS

Abstract. The multi-sensor fusion scheme has become more and more popular these days with its great potential to estimate reliable navigation information for the modern development in automated driving system (ADS) and mobile mapping systems (MMS). Since these systems are combined with numerous navigation sensors, thus their geometric relationship should be precisely known. This study focuses on practical aspects when calibrating LiDAR-IMU mounting parameters (lever-arms and bore-sight angles) in land-based MMS. This calibration model is based on expressing the mounting parameters within the direct georeferencing equation for each epoch time and conditioning a set of INS/GNSS and LiDAR navigation solutions to lie on it. There is no need for a required information about the planar features in the calibration field as part of the unknowns. Such conditions are only benefitable in the residential area where the presence of sufficient planes in form of building is abundant. We present an approach for recovery the mounting parameters by conditioning the high-definition (HD) point cloud map-based LiDAR information and INS/GNSS navigation solutions through the least-squares solutions. The presented results and discussion mainly focus on practical examples with data from land-based MMS. Preliminary results indicate that correct calibration parameters are not only capable to improve the performance of point cloud georeferencing but also dramatically provide reliable performance evaluation of navigation estimation. Moreover, these findings show that the studied method is not only applicable in the featureless environment but also in its practicality to the self-driving applications.

A COMBINED APPROACH FOR LONG-TERM MONITORING OF BENTHOS IN ANTARCTICA WITH UNDERWATER PHOTOGRAMMETRY AND IMAGE UNDERSTANDING

Abstract. Long-term monitoring projects are becoming more than ever crucial in assessing the effects of climate change on marine communities, especially in Antarctica, where these changes are expected to be particularly dramatic. Detailed studies of the Antarctic benthos are in fact particularly important for a better understanding of benthos dynamics and potential climate-driven shifts. Here, due to the extreme fragility of benthic communities, non-destructive techniques represent the best solution in long-term monitoring programs. In this paper we report new results from 2017, 2018, 2019 photogrammetric campaigns within the Italian National Antarctic Research Program (PNRA). A new protocol of data acquisition and multi-temporal processing that provides co-registered 3D point clouds between the three years without control points nor direct georeferencing methods is presented. This is achieved by adding a level of image understanding leveraging semantic segmentation with convolutional neural network (CNN) of the benthic features. Slow growing (estimated less than a mm per year) organisms, such as Corallinales (Rhodophyta algae), represent a natural stable pattern, leveraged to automatically orient in the same reference system the photogrammetric surveys of different epochs. This approach is also proved to be effective in improving the orientation of adjacent strips acquired within the same campaign. Within the paper an in depth analysis of the achieved results shows the effectiveness of the implemented procedure.

CIRCULAR TARGET OBSERVATION OF POINT CLOUD USING LASER REFLECTION INTENSITY BY AN UNMANNED AERIAL VEHICLE EQUIPPED WITH A LASER SCANNER

Abstract. Unmanned aerial vehicles (UAVs) equipped with laser scanners have been widely used for various purposes, such as in construction sites, forestry, and disaster management, as they can obtain high density point clouds with millimeter to centimeter scale accuracy. However, systematic errors in the height of a UAV relative to the earth's surface may occur owing to the method of direct georeferencing using the global navigation system satellite (GNSS) and inertial measurement unit (IMU). Therefore, to enable highly accurate surveying, adjustments must be made using ground control points. However, interpreting the ground control points of a discrete, inhomogeneous density point cloud requires a high degree of skill and effort. In this study, a high-end UAV laser scanner was used to obtain the point clouds of a site using a white circular target enclosed within a black frame in a measurement setup of 500 points/m2. The center coordinates of the circle, calculated using the conventional and proposed methods, were evaluated qualitatively and quantitatively. As a result, the average of 10 error distances was found to be 0.028 m for the weighted center of gravity method, 0.014 m for the fitting circle equation method, and 0.008 m for the proposed method. These results corresponded to one-fifth to one-half of the 0.045-m point intervals of the measurement plan. Thus, using the reflection intensity of point clouds, the circular target observation could be performed.

ODYN: AN ONLINE DYNAMIC NETWORK SOLVER FOR PHOTOGRAMMETRY AND LIDAR GEO-REFERENCING

Abstract. In recent years, many methods have been proposed to improve the quality of the estimated trajectories for airborne or terrestrial mapping platforms leveraging multi-sensor fusion. One of the motivating applications is, for example, the use laser scanners on small unmanned aerial vehicles, where the typically employed low-cost MEMS inertial sensors do not allow for satisfactory direct geo-referencing of the laser points. In this work we introduce ODyN, an online Dynamic Network solver that can fuses information from cameras, GNSS and inertial sensors in a single adjustment. It can be employed to estimate a high-frequency trajectory for precise direct geo-referencing, to improve photogrammetric reconstructions in challenging scenarios or to determine several types of system calibration parameters. The presented solver is hosted by the University of Geneva and is free for anybody to use. In this work we present a use case in airborne mapping where the obtained trajectory estimates are improved with respect to the recursive smoothing approaches conventionally used in direct geo-referencing.

AUTOMATIC POLE DETECTION IN AERIAL AND SATELLITE IMAGERY FOR PRECISE IMAGE REGISTRATION WITH SAR GROUND CONTROL POINTS

Abstract. The world-wide absolute geographic positioning accuracy of optical Satellite imagery is mostly about a few pixels of the image resolution. So for example WorldView-3 images have a CE90 of about 4 m. Also the direct georeferencing without ground control information of aerial imagery is in the same range of one to a few metres. These inaccuracies originate predominantly in uncertainties of angular measurements for the sensor attitude. An angular error of only one arc-second at a satellite 750 km above ground results in an absolute error on ground of 3.6 metres. On the other hand radar satellites like TerraSAR-X or TanDEM-X do not measure angles but signal runtimes. So if we identify the same point in an optical image and in a radar image we can solve the problem of inaccurate angle-measurements in the optical sensor models and are able to georeference optical images world wide absolute to below one pixel. In this paper we present a method for identification of point-objects which can be detected in both types of images: the footpoints of poles. If such a footpoint of a pole can be detected simultaneously in both types of images the geoposition of the optical image can be corrected to the accuracy of the point-measurement in the radar image. To achieve a high accuracy also a nearly perfect correction of all errors in signal propagation times of the radar signals has to be conducted. In this paper we describe how the footpoints of poles will be extracted in optical spaceborne or air-borne imagery and how these footpoints are correlated to the potential footpoints of poles detected in the radar imagery.

The challenges and opportunities of using small drones to monitor fishing activities in a marine protected area

AbstractFishing monitoring to gather fishery dynamic data is usually land‐based or carried from onboard inspections. However, direct georeferencing of fishing activities using aerial drones is a versatile and easy option that can potentially improve fishery studies, especially in Marine Protected Areas. We investigated the utility of aerial images to understand spatial displacement and fishing territoriality in a Brazilian MPA system under risk of deregulation. Survey flights were conducted in the multiple use area where fishing is allowed in the Fernando de Noronha Archipelago, located in the South Atlantic Ocean and considered one of the main ecotourism destinations and marine conservation sites in Brazil. An aircraft was used to map and detect several aspects of fishing activities and shallow seabed characteristics to demonstrate its utility for fishery management. We recorded 89 fishing activities in the marine area and 75 shore fishers on beaches. Results documented areas most intensively targeted by fishers, and operational aspects of fishing effort, both onboard and shoreline activities targeting shoals. Enforcement and monitoring efforts must be strengthened to assess the impact of expanded fishing activity in this marine sanctuary, mainly due to fading conservation rules imposed by top‐down political decisions. This aerial approach demonstrates the utility of quantifying and mapping fishing activities to assist both MPA and fisheries management aligned to benchmarks for biodiversity conservation and human‐based demands.

Hybrid georeferencing of images and LiDAR data for UAV-based point cloud collection at millimetre accuracy

During the last two decades, UAV emerged as standard platform for photogrammetric data collection. Main motivation in that early phase was the cost effective airborne image collection at areas of limited size. This was already feasible by rather simple payloads like an off-the-shelf, compact camera and a navigation-grade GNSS sensor. Meanwhile, dedicated sensor systems enable applications that have not been feasible in the past. One example is the airborne collection of dense 3D point clouds at millimetre accuracies, which will be discussed in our paper. For this purpose, we collect both LiDAR and image data from a joint UAV platform and apply a so-called hybrid georeferencing. This process integrates photogrammetric bundle block adjustment with direct georeferencing of LiDAR point clouds. By these means georeferencing accuracy is improved for the LiDAR point cloud by an order of magnitude. We demonstrate the feasibility of our approach in the context of a project, which aims on monitoring of subsidence of about 10 mm/year. The respective area of interest is defined by a ship lock and its vicinity of mixed use. In that area, multiple UAV flights were captured and evaluated for a period of three years. As our main contribution, we demonstrate that 3D point accuracies at sub-centimetre level can be achieved. This is realized by joint orientation of laser scans and images in a hybrid adjustment framework, which enables accuracies corresponding to the GSD of the captured imagery.

Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation

More accurate snow quality predictions are needed to economically and socially support communities in a changing Arctic environment. This contrasts with the current availability of affordable and efficient snow monitoring methods. In this study, a novel approach is presented to determine spatial snow depth distribution in challenging alpine terrain that was tested during a field campaign performed in the Tarfala valley, Kebnekaise mountains, northern Sweden, in April 2019. The combination of a multispectral camera and an Unmanned Aerial Vehicle (UAV) was used to derive three-dimensional (3D) snow surface models via Structure from Motion (SfM) with direct georeferencing. The main advantage over conventional photogrammetric surveys is the utilization of accurate Real-Time Kinematic (RTK) positioning which enables direct georeferencing of the images, and therefore eliminates the need for ground control points. The proposed method is capable of producing high-resolution 3D snow-covered surface models (<7 cm/pixel) of alpine areas up to eight hectares in a fast, reliable and affordable way. The test sites’ average snow depth was 160 cm with an average standard deviation of 78 cm. The overall Root-Mean-Square Errors (RMSE) of the snow depth range from 11.52 cm for data acquired in ideal surveying conditions to 41.03 cm in aggravated light and wind conditions. Results of this study suggest that the red components in the electromagnetic spectrum, i.e., the red, red edge, and near-infrared (NIR) band, contain the majority of information used in photogrammetric processing. The experiments highlighted a significant influence of the multi-spectral imagery on the quality of the final snow depth estimation as well as a strong potential to reduce processing times and computational resources by limiting the dimensionality of the imagery through the application of a Principal Component Analysis (PCA) before the photogrammetric 3D reconstruction. The proposed method is part of closing the scale gap between discrete point measurements and regional-scale remote sensing and complements large-scale remote sensing data and snow model output with an adequate validation source.

Direct Georeferencing UAV-SfM in High-Relief Topography: Accuracy Assessment and Alternative Ground Control Strategies along Steep Inaccessible Rock Slopes

Steep rock slopes present key opportunities and challenges within Earth science applications. Due to partial or complete inaccessibility, high-precision surveys of these high-relief landscapes remain a challenge. Direct georeferencing (DG) of unoccupied aerial vehicles (UAVs) with advanced onboard GNSS receivers presents opportunities to generate high-resolution 3D datasets without ground-based access to the study area. However, recent research has revealed large vertical errors using DG that may prove problematic in near-vertical terrain. To address these concerns, we examined more than 75 photogrammetric UAV-datasets with various imaging angles (nadir, oblique, and combinations) and ground control scenarios, including DG, along a steep slope exposure. Results demonstrate that mean errors in DG scenarios are up to 0.12 m higher than datasets using integrated georeferencing with well-distributed GCPs. Inclusion of GCPs greatly reduced mean error values but had limited influence on precision (&lt;0.01 m) for any given imaging strategy. Use of multiple image angles resulted in the highest precisions, regardless of georeferencing strategy. These findings have implications for applications requiring the highest precision and accuracy (e.g., geotechnical engineering, hazard mitigation and mapping, and geomorphic change detection), which should consider using ground control whenever possible. However, for applications less concerned with absolute accuracy, our results show that DG datasets provide strong internal consistency and relative accuracy that may be suitable for high precision measurements within a model, without use of ground control.

Accuracy Assessment of a UAV Direct Georeferencing Method and Impact of the Configuration of Ground Control Points

Unmanned aerial vehicles (UAVs) can obtain high-resolution topography data flexibly and efficiently at low cost. However, the georeferencing process involves the use of ground control points (GCPs), which limits time and cost effectiveness. Direct georeferencing, using onboard positioning sensors, can significantly improve work efficiency. The purpose of this study was to evaluate the accuracy of the Global Navigation Satellite System (GNSS)-assisted UAV direct georeferencing method and the influence of the number and distribution of GCPs. A FEIMA D2000 UAV was used to collect data, and several photogrammetric projects were established. Among them, the number and distribution of GCPs used in the bundle adjustment (BA) process were varied. Two parameters were considered when evaluating the different projects: the ground-measured checkpoints (CPs) root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2) distance. The results show that the vertical and horizontal RMSE of the direct georeferencing were 0.087 and 0.041 m, respectively. As the number of GCPs increased, the RMSE gradually decreased until a specific GCP density was reached. GCPs should be uniformly distributed in the study area and contain at least one GCP near the center of the domain. Additionally, as the distance to the nearest GCP increased, the local accuracy of the DSM decreased. In general, UAV direct georeferencing has an acceptable positional accuracy level.

Comprehensive Direct Georeferencing of Aerial Images for Unmanned Aerial Systems Applications.

Optical image sensors are the most common remote sensing data acquisition devices present in Unmanned Aerial Systems (UAS). In this context, assigning a location in a geographic frame of reference to the acquired image is a necessary task in the majority of the applications. This process is denominated direct georeferencing when ground control points are not used. Despite it applies simple mathematical fundamentals, the complete direct georeferencing process involves much information, such as camera sensor characteristics, mounting measurements, attitude and position of the UAS, among others. In addition, there are many rotations and translations between the different reference frames, among many other details, which makes the whole process a considerable complex operation. Another problem is that manufacturers and software tools may use different reference frames posing additional difficulty when implementing the direct georeferencing. As this information is spread among many sources, researchers may face difficulties on having a complete vision of the method. In fact, there is absolutely no paper in the literature that explain this process in a comprehensive way. In order to supply this implicit demand, this paper presents a comprehensive method for direct georeferencing of aerial images acquired by cameras mounted on UAS, where all required information, mathematical operations and implementation steps are explained in detail. Finally, in order to show the practical use of the method and to prove its accuracy, both simulated and real flights were performed, where objects of the acquired images were georeferenced.

Towards precise drone-based measurement of elevation change in permafrost terrain experiencing thaw and thermokarst

Measuring ground elevation changes plays a crucial role in several environmental applications. For instance, permafrost soils undergo seasonal active layer freezing and thawing that causes cyclic elevation changes. Permafrost thaw can result in unidirectional ground subsidence, which may be gradual and uniform, or rapid and irregular in the case of thermokarst landforms such as slumps and degrading ice-wedges. Photogrammetric drone surveys have effectively characterized large (&gt; 0.1 m) ground elevation changes resulting from thermokarst, yet many permafrost processes of interest lead to more subtle elevation changes. In this study, we assessed various drone-based surveying strategies for their precision to measure smaller (&lt; 0.1 m) ground elevation changes to better characterize permafrost-driven surface dynamics. The strategies were compared by examining the short-term reproducibility of modeled elevation for 76 bare ground targets, derived from six repeat drone surveys captured under variable illumination. We found that the Phantom 4 RTK drone using direct georeferencing, combined with one fixed ground control point, could reproduce elevations with a mean absolute deviation of 0.6 cm, suggesting a minimum level of change detection of 1.4 cm at 95% confidence. Drone-based methods for measuring permafrost elevation changes should be complementary to in situ and satellite-based (e.g. differential interferometric Synthetic Aperture Radar) approaches.

Application of UAV Photogrammetry to Slope-Displacement Measurement

To reduce accidents caused by slope failures, disaster assessments and displacement measurements of steep slopes are conducted. Measurement values can vary greatly, depending on the technician's proficiency. Moreover, land creep and minute displacements are difficult to detect. In this study, we attempted to solve this problem using unmanned aerial vehicles (UAVs) and aerial triangulation. After a review of previous studies, a measurement precision of 10 cm for daily monitoring and sub-centimeters for precise monitoring were set. Twelve checkpoints were used, and the comparison results between the measured values using the total station and the aerial triangulation using the UAV were as follows. A virtual reference station (VRS) and three ground control points (GCPs) yielded 11.58 mm for the 3D (root-mean-square error) RMSE, and post-processed kinematic (PPK) and three GCPs generated 9.71 mm. By differentiating the results of multiple observations, the PPK processing results could achieve sub-centimeter accuracy. We verified that direct georeferencing using a VRS is sufficient for daily monitoring, and precise measurements require at least three GCPs. Geotechnicians can use VRS observations for economical slope monitoring in normal times, and accurate observations with GCPs when precise measurements are required.

A hierarchical approach for refining point cloud quality of a low cost UAV LiDAR system in the urban environment

Insufficient accuracies of the low end position and orientation system (POS) used in low cost UAV LiDAR systems (ULSs) cause the direct georeferencing data to lead to poor point cloud quality. Trajectory correction and scan-to-map matching are two commonly used strategies for point cloud quality refinement. The existing trajectory correction strategies work with the assumption that POS errors can be modeled as a time-variant function, which cannot be applied to the low end POS. The existing scan-to-map matching methods have difficulty refining the ULS point clouds due to the large gaps between scan lines. This paper proposes HR-ULS, hierarchical refinement for low cost ULS point cloud quality in the urban environment, to solve these challenges. HR-ULS separated the raw laser scanning point clouds into a set of scan-blocks and refined the point cloud quality with a hierarchical strategy, resulting in local and global optimization, respectively. First, the internal scan-block matching (ISBM) estimated multiscale distributions for each laser frame and calculated relative motions iteratively to achieve local map consistency in each scan-block. Second, the multiview scan-block matching (MSBM) took inertial, Global Navigation Satellite System (GNSS), and laser measurements into a unified adjustment framework to correct the trajectory, achieving global map consistency between scan-blocks. Comprehensive experiments evaluated the proposed HR-ULS with the point clouds captured by a low-cost ULS in three typical urban areas. They showed that the average plane fitting RMSE of the ULS point clouds was improved from 0.34 m to 0.09 m, and the average checkpoint offset was improved from 1.86 m to 0.21 m, achieving an identical level of accuracy with that of direct georeferencing using a high end POS, APX-15-UAV.

Performance of UAV-Based Digital Orthophoto Generation for Emergency Response Applications

Unmanned Aerial Vehicles (UAVs) have been used for accurate orthophoto generation based on advanced Global Navigation Satellite System (GNSS) techniques. In recent years, the UAV systems have become an effective tool for fast monitoring of damages caused by disasters such as the earthquake hazards. The conventional orthophoto generation based on ground control points takes too much time during emergency situations. In the study, different methodologies for the processing of the acquired GNSS Positioning data for direct georeferencing of UAVs were investigated in terms of various orbit products. Evaluating the fitness for emergency response applications, the ground control points (GCPs) also used for validation of the generated orthophoto without using GCPs and based on Precise Point Positioning (PPP) approach. In this study, Ultra-Rapid, Rapid and Final PPP methods based on GNSS observations were used for direct geo-referencing. Thirteen GCPs were located at the study area for the validation of the orthophoto accuracy generated by direct geo-referencing.

Seamless Mosaicking of UAV-Based Push-Broom Hyperspectral Images for Environment Monitoring

This paper proposes a systematic image mosaicking methodology to produce hyperspectral image for environment monitoring using an emerging UAV-based push-broom hyperspectral imager. The suitability of alternative methods in each step is assessed by experiments of an urban scape, a river course and a forest study area. First, the hyperspectral image strips were acquired by sequentially stitching the UAV images acquired by push-broom scanning along each flight line. Next, direct geo-referencing was applied to each image strip to get initial geo-rectified result. Then, with ground control points, the curved surface spline function was used to transform the initial geo-rectified image strips to improve their geometrical accuracy. To further remove the displacement between pairs of image strips, an improved phase correlation (IPC) and a SIFT and RANSAC-based method (SR) were used in image registration. Finally, the weighted average and the best stitching image fusion method were used to remove the spectral differences between image strips and get the seamless mosaic. Experiment results showed that as the GCPs‘ number increases, the mosaicked image‘s geometrical accuracy increases. In image registration, there exists obvious edge information that can be accurately extracted from the urban scape and river course area; comparative results can be achieved by the IPC method with less time cost. However, for the ground objects with complex texture like forest, the edges extracted from the image is prone to be inaccurate and result in the failure of the IPC method, and only the SR method can get a good result. In image fusion, the best stitching fusion method can get seamless results for all three study areas. Whereas, the weighted average fusion method was only useful in eliminating the stitching line for the river course and forest areas but failed for the urban scape area due to the spectral heterogeneity of different ground objects. For different environment monitoring applications, the proposed methodology provides a practical solution to seamlessly mosaic UAV-based push-broom hyperspectral images with high geometrical accuracy and spectral fidelity.

Depth-camera-based rebar detection and digital reconstruction for robotic concrete spraying

In this paper, we tackle the challenge of detection and accurate digital reconstruction of steel rebar meshes using a set of industrial depth cameras. A construction example under investigation in this paper is robotic concrete spraying, where material is sprayed onto double-curved single layered rebar meshes. Before the spraying process can start, the location and geometry of the rebar mesh needs to be accurately know. We present an automatic image-based processing approach of depth images for grid point extraction at an accuracy of a few mm. Furthermore, we propose a sequence of execution steps in a robotic setup, including the hand–eye calibration, which enables the direct georeferencing of multiple data sets acquired from various poses into a common coordinate system. With the proposed approach we are able to digitally reconstruct a mesh of an unknown geometry in under 10 min with an accuracy better than 5 mm. The digitally reconstructed mesh allows for computation of material needed for its construction, enabling sustainable use of concrete in digital fabrication. The accurately reconstructed digital mesh, generated based on the proposed approach in this paper, is the input for the following spraying step, allowing for generation of accurate spray trajectories.

Study Progress of Important Agricultural Heritage Systems (IAHS): A Literature Analysis

Important Agricultural Heritage Systems (IAHS), as a new type of heritage, has received extensive attention from the international scientific communities. With the increase of IAHS research, reviews on it have been conducted by many scholars. However, visualized research to show future research trends of IAHS are lacking. Therefore, using metrology analysis methods, this study aims at presenting the progress of research and the general development trends of Globally Important Agricultural Heritage Systems (GIAHS) in the world from 2006 to 2020 to provide ideas for the development of countries or regions in the future. This study mapped 292 literatures from Web of Science core collections from 2006 to 2020 by CiteSpace software. The results show that research on IAHS from 2006 to 2020 experienced two stages: the fluctuating increase stage, and the steady growth stage. Author groups from China, Italy, the USA, Japan, etc., contributed many papers on IAHS. Institutions including the Chinese Academy of Sciences, the University of Florence and the University of Padua in Italy, etc., have a relatively high influence on international IAHS research. Agriculture Ecosystems &amp; Environment is the most cited journal. Agricultural Heritage Systems, regeneration, agriculture, agroforestry, dry-stone wall, social capital, instability, and agricultural biodiversity have been hotspots in the past 15 years. The research themes mainly focus on GIAHS, tourism, livelihood assets, and direct georeferencing. Authors in different regions concern different research themes. In the future, the fields of applications and microscopic views, social sciences, applications of standardized quantitative research methods, and broadened international cooperation should be paid more attention.

Quality Analysis of Direct Georeferencing in Aspects of Absolute Accuracy and Precision for a UAV-Based Laser Scanning System

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 &lt;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 &lt;2 cm for the horizontal direction and &lt;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.