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 <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.

A DATA COLLECTION FRAMEWORK FOR MANAGING ACCESSIBILITY AND INCLUSION IN URBAN HERITAGE

The successful implementation of inclusive design strategies cannot overlook the development of a preliminary phase aimed at gathering accessibility data of the built environment. This set of information helps achieve two major objectives: planning measures for improving the fruition of a city and communicating to end users the opportunities to exploit places. Specifically, this is fundamental in Cultural Heritage contexts both to survey their specific features and convey their historical values. To this end, such information must be accurate and gathered quickly. This paper aims to provide a set of parameters through which it is possible to comprehensively assess accessibility of Urban Heritage environments. Particularly, such task has been carried out in a more general framework targeted to investigate, how and by which tools, the current design practice achieve the aforementioned objectives. The article proposes a geometric survey through Mobile Laser Scanning system as a data gathering tool. The semantic segmentation of the resulting point cloud is envisioned as a suitable method for the extraction of the accessibility parameters proposed. Basing on first tests applied on a case study, a UNESCO site, the article provides and discusses a final proposal for the best data processing and validation, in addition to the key tools for sharing this information.

HYBRID 3D PRINTER FOR LAYER-BY-LAYER FORMATION OF STRUCTURES WITH CONDUCTIVE TOPOLOGY

The paper presents the main characteristics and functionality of a hybrid 3D-printer created at the Institute of Automation and Electrometry SB RAS, containing a portal recording system with dispenser heads for digital inkjet printing and a laser scanning system for subsequent post-processing with precise alignment software. The formation zone is located on a mobile platform moving along the Z coordinate. This design makes it possible, by layer-by-layer additive synthesis, to form three-dimensional structures with given local conductivity.

Under-Canopy UAV Laser Scanning Providing Canopy Height and Stem Volume Accurately

Automation of forest field reference data collection has been an intensive research objective for laser scanning scientists ever since the invention of terrestrial laser scanning more than two decades ago. Recently, it has been proposed that such automated data collection providing both the tree heights and stem curves would require a combination of above-canopy UAV point clouds and terrestrial point clouds. In this study, we demonstrate that an under-canopy UAV laser scanning system utilizing a rotating laser scanner can alone provide accurate estimates of the canopy height and the stem volume for the majority of the trees in a boreal forest. To this end, we mounted a rotating laser scanner based on a Velodyne VLP-16 sensor onboard a manually piloted UAV. The UAV was commanded with the help of a live video feed from the onboard camera of the UAV. Since the system was based on a rotating laser scanner providing varying view angles, all important elements such as treetops, branches, trunks, and ground could be recorded with laser hits. In an experiment including two different forest structures, namely sparse and obstructed canopy, we showed that our system can measure the heights of individual trees with a bias of -20 cm and a standard error of 40 cm in the sparse forest and with a bias of -65 cm and a standard error of 1 m in the obstructed forest. The accuracy of the obtained tree height estimates was equivalent to airborne above-canopy UAV surveys conducted in similar forest conditions. The higher underestimation and higher inaccuracy in the obstructed site can be attributed to three trees with a height exceeding 25 m and the applied laser scanning system VLP-16 that had a limited height measurement capacity when it comes to trees taller than 25 m. Additionally, we used our system to estimate the stem volumes of individual trees with a standard error at the level of 10%. This level of error is equivalent to the error obtained when merging above-canopy UAV laser scanner data with terrestrial point cloud data. Future research is needed for testing new sensors, for implementing autonomous operation inside canopies through collision avoidance and navigation through canopies, and for developing robust methods that work also with more complex forest structure. The results show that we do not necessarily need a combination of terrestrial point clouds and point clouds collected using above-canopy UAV systems in order to accurately estimate the heights and the volumes of individual trees.

Hybrid laser-assisted forming process for ultralight three-dimensional glass elements

As part of a research project, a new hybrid forming process for ultrathin three-dimensional glass elements was developed. In a continuous process chain, the three-part procedure enables large-area gravity-assisted bending of thin glass, partial deep-drawing of individual small structures using CO2 laser radiation and cutting of flexible inner and outer contours. Using an experimental setup with a laser scanning system, novel component designs can be generated in a highly flexible and reproducible manner. This can open up new application fields or expand existing ones, for example in the area of cover glasses for display technology.

An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials

The measurement of forestry trials is a costly and time-consuming process. Over the past few years, unmanned aerial vehicles (UAVs) have provided some significant developments that could improve cost and time efficiencies. However, little research has examined the accuracies of these technologies for measuring young trees. This study compared the data captured by a UAV laser scanning system (ULS), and UAV structure from motion photogrammetry (SfM), with traditional field-measured heights in a series of forestry trials in the central North Island of New Zealand. Data were captured from UAVs, and then processed into point clouds, from which heights were derived and compared to field measurements. The results show that predictions from both ULS and SfM were very strongly correlated to tree heights (R2 = 0.99, RMSE = 5.91%, and R2 = 0.94, RMSE = 18.5%, respectively) but that the height underprediction was markedly lower for ULS than SfM (Mean Bias Error = 0.05 vs. 0.38 m). Integration of a ULS DTM to the SfM made a minor improvement in precision (R2 = 0.95, RMSE = 16.5%). Through plotting error against tree height, we identified a minimum threshold of 1 m, under which the accuracy of height measurements using ULS and SfM significantly declines. Our results show that SfM and ULS data collected from UAV remote sensing can be used to accurately measure height in young forestry trials. It is hoped that this study will give foresters and tree breeders the confidence to start to operationalise this technology for monitoring trials.