Precise digital twin production by UAV and TLS data fusion

ISPRS International Journal of Geo-Information, 2018

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and po...

Both unmanned aerial vehicle (UAV) technology and Mobile Mapping Systems (MMS) are important techniques for surveying and mapping. In recent years, the UAV technology has seen tremendous interest, both in the mapping community and in many other fields of application. Carrying off-the shelf digital cameras, the UAV can collect high quality aerial optical images for city modeling using photogrammetric techniques. In addition, a MMS can acquire high density point clouds of ground objects along the roads. The UAV, if operated in an aerial mode, has difficulties in acquiring information of ground objects under the trees and along façades of buildings. On the contrary, the MMS collects accurate point clouds of objects from the ground, together with stereo images, but it suffers from system errors due to loss of GPS signals, and also lacks the information of the roofs. Therefore, both technologies are complementary. This paper focuses on the integration of UAV images, MMS point cloud data and terrestrial images to build very high resolution 3D city models. The work we will show is a practical modeling project of the National University of Singapore (NUS) campus, which includes buildings, some of them very high, roads and other man-made objects, dense tropical vegetation and DTM. This is an intermediate report. We present work in progress.

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019

A small unmanned aerial vehicle (UAV) with survey-grade GNSS positioning is used to produce a point cloud for topographic mapping and 3D reconstruction. The objective of this study is to assess the accuracy of a UAV imagery-derived point cloud by comparing a point cloud generated by terrestrial laser scanning (TLS). Imagery was collected over a 320m by 320m area with undulating terrain, containing 80 ground control points. A SenseFly eBee Plus fixed-wing platform with PPK positioning with a 10.6mm focal length and a 20MP digital camera was used to fly the area. Pix4Dmapper, a computer vision based commercial software, was used to process a photogrammetric block, constrained by 5 GCPs while obtaining cm-level RMSE based on the remaining 75 checkpoints. Based on results of automatic aerial triangulation, a point cloud and digital surface model (DSM) (2.5 cm/pixel) are generated and their accuracy assessed. A bias less than 1 pixel was observed in elevations from the UAV DSM at the checkpoints. 31 registered TLS scans made up a point cloud of the same area with an observed horizontal root mean square error (RMSE) of 0.006m, and negligible vertical RMSE. Comparisons were made between fitted planes of extracted roof features of 2 buildings and centreline profile comparison of a road in both UAV and TLS point clouds. Comparisons showed an average +8-cm bias with UAV point cloud computing too high in two features. No bias was observed in the roof features of the southernmost building.

Lidar Remote Sensing for Environmental Monitoring 2017, 2017

Geospatial information is data on the position of natural and artificial objects existing in space and is usually displayed in map form. Recently, virtual reality (VR) and augmented reality (AR) technologies have been utilized in various fields, and the importance of constructing three-dimensional spatial information as a basis of these technologies has increased. Light detection and ranging (LiDAR) technology is increasingly used to acquire data on terrain. Because LiDAR can generate a high-density point cloud while saving time and manpower consumption, it is increasingly utilized for 3D model building, measurement of structural deformation, and drawing construction for building maintenance. On the other hand, unmanned aerial vehicles (UAVs) are increasingly used for acquiring building geospatial information. In this study, geospatial information construction using UAV images and UAV LiDAR data was performed. Fixed-wing image acquisition and processing using UAV were performed. Data acquired using UAV LiDAR were used to construct a digital surface model (DSM), and the accuracy and characteristics of images and LiDAR data were analyzed. In the future, the construction of DSM using UAV LiDAR will greatly increase the efficiency of related field work.

The main focus of the paper is a comparative study in which we have investigated, whether automatically generated digital surface models (DSM) obtained from unmanned aerial systems (UAS) imagery are comparable with DSM obtained from terrestrial laser scanning (TLS). The research is conducted at a pilot dike for coastal engineering. The effort and the achievable accuracy of both DSMs are compared. The error budgets of these two methods are investigated and the models obtained in each case compared against each other.

2021

The request for 3D Data for the use of 3D city-models is increasing rapidly. More and more tools are able to deal with data of several sensors, out of video-streams, oblique camera setups with huge overlaps as well as terrestrial data. To achieve high accuracy of the data and a fast processing pipeline, a smart workflow has to be defined and established. However, mixed data sources are still a challenge especially if different sensors with an extremely different GSD are used. This abstracts demonstrates such a workflow, the processing pipeline and the challenges in a mixed data processing. Special calibration and co-calibrating procedures have been applied to get model in model solution managed to solve the dual task of 3D city mapping and cultural heritage conservation. Especially the sensor setup directly influences the geometric accuracy of the product. To do missions for 2-5 cm GSD, metric systems are indispensable while for non-metric applications also simple and cheaper sensor...

the 36th International Symposium on Automation and Robotics in Construction (ISARC), 2019

The characteristics of dynamic construction sites increase the difficulty of collecting the high-quality geometric data necessary to achieve construction management activities. This paper introduces a new autonomous framework for 3D geometric data collection in a dynamic cluttered environment using an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). This method first deploys UAV to collect photo images of a site and builds a point cloud of the 3D terrain of the site, including obstacle information. A mesh grid is then created from the UAV-generated point cloud, and simulation for laser-scan planning is conducted to determine the stationary laser-scan positions at which a mobile robot can collect data with less occluded views while capturing all crucial geometric information. Finally, optimal paths for the UGV to navigate among the estimated scan positions are generated. Promising test results regarding data accuracy and collection time show that the proposed collaborative UAV-UGV approach can significantly reduce human intervention and provide technologies for enabling construction site to be frequently monitored, updated, and analyzed for timely decision-making.

Remote Sensing, 2020

This paper provides the innovative approach of using a spatial extract, transform, load (ETL) solution for 3D building modelling, based on an unmanned aerial vehicle (UAV) photogrammetric point cloud. The main objective of the paper is to present the holistic workflow for 3D building modelling, emphasising the benefits of using spatial ETL solutions for this purpose. Namely, despite the increasing demands for 3D city models and their geospatial applications, the generation of 3D city models is still challenging in the geospatial domain. Advanced geospatial technologies provide various possibilities for the mass acquisition of geospatial data that is further used for 3D city modelling, but there is a huge difference in the cost and quality of input data. While aerial photogrammetry and airborne laser scanning involve high costs, UAV photogrammetry has brought new opportunities, including for small and medium-sized companies, by providing a more flexible and low-cost source of spatial...

ABTRACT : Optical imagery and range data can be registered to create photo-realistic scene models via texture mapping. Presented in this paper is an alternative approach where true colour (RBG) point clouds are generated by automatically fusing a close-range optical (RGB) image acquired with an uncalibrated digital camera with the corresponding high-density 3D lidar point cloud collected with a terrestrial laser scanner (TLS). The alignment of optical pixel colour values and lidar point cloud is obtained by estimating the position and orientation of the camera with respect to the lidar point cloud reference system. To perform this sensor co-registration, an automated corner feature extraction algorithm, followed by area-based image matching is applied between the optical data and the lidar intensity image to establish point correspondence. The matching process is solely based on point matches and does not use external control or calibration patterns. The 3D lidar points of the corresponding lidar intensity image corner points are then extracted from the point cloud. As these 3D lidar points correspond to the extracted optical image corner points, a bundle self-calibration adjustment with additional parameters is applied using the extended collinearity equations to estimate the interior and exterior orientation of the camera. The RANSAC robust estimator is used to reduce the influence of outliers in the estimation of the camera parameters. Having established the mathematical relationship between image space and lidar points a photo-realistic 3D model is generated. Through reverse mapping, each point in the lidar point cloud is assigned the RGB value of the image pixel upon which it is projected. Experiments are performed observing typical urban scenes, particularly building facades. The feasibility and potential of estimating the co-registration parameters using a TLS is evaluated in terms of accuracy of the results. The true calibration parameters, provided by the TLS manufacturer, are used in the validation of the registration parameters. The technique has reliably aligned a camera with the TLS geometry for the simultaneous generation of point based photo-realistic 3D models.

III. International Istanbul Current Scientific Research Congress, 2023

Unmanned aerial vehicle (UAV) systems have become frequently utilized in scientific studies in recent years, thanks to many advantages of low flight altitude. An important factor in the popularity of UAV systems is that high-resolution data can be obtained periodically, at a lower cost than traditional airborne remote sensing methods, in study areas of suitable width. Optical UAV is the most preferred UAV technology because it allows the production of detailed and original color three-dimensional (3D) models of objects with a wide variety of heights and forms, thanks to its ability to work with high-resolution digital cameras at different altitudes and in superimposed shooting geometries. However, operating with the principle of passive remote sensing brings with it the dependence on sunlight and the inability to operate outside of daylight hours. In addition, the lack of vegetation penetration capability in wide wavelength radar or laser systems creates serious problems in 3D depiction, especially in regions under forest cover. In addition, limitations in the mapping and orientation of aerial photographs, created by the image acquisition geometry and low correlation land cover in the study area, bring with it errors and incomplete depiction of the dense point clouds and 3D models generated. In this study, problematic points and solution approaches are presented in 3D model generation from DJI Phantom 4 Pro V2.0 data, which has become one of the most preferred commercial optical UAVs by users due to the advantages of the 20 MP Sony Exmor RGB camera. In the study, different effects caused by the parameters used in the 3D topographic description and object description in SfM (structure from motion) based image matching software were presented.