Calibration and accuracy assessment in a direct georeferencing system for UAS photogrammetry

Abstract

Unmanned aerial systems (UAS) have already proven useful in fields and disciplines such as agriculture, forestry, or environmental mapping, and they have also found application during natural and nuclear disasters. In many cases, the environment is inaccessible or dangerous for a human being, meaning that the widely used technique of aerial imagery georeferencing via ground control points cannot be employed. The present paper introduces a custom-built multi-sensor system for direct georeferencing, a concept that enables georeferencing to be performed without an access to the mapping area and ensures centimetre-level object accuracy. The proposed system comprises leading navigation system technologies in the weight category of micro and light UASs. A highly accurate global navigation satellite system receiver integrating the real time kinematic technology supports an inertial navigation system where data from various sensors are fused. Special attention is paid to the time synchronisation of all sensors, and a method for the field calibration of the system is designed. The multi-sensor system is completely independent of the used UAS.The authors also discuss the verification of the proposed system's performance on a real mission. To make the results credible, a high number of test points are used, with both the direct and the indirect georeferencing techniques subjected to comparison, together with different calibration methods. The achieved spatial object accuracy (about 4 cm RMSE) is sufficient for most applications.

The use of unmanned aerial vehicles (UAVs) for engineering geology applications

“…In some cases, an external GNSS receiver can be installed and used to collect the raw data of the UAV path. This solution is adopted for direct photogrammetry applications Eling et al 2015;Mian et al 2015;Gabrlik et al 2018), where is required a high-resolution GNNS on board that reduce the importance and the impact of GCPs on the final accuracy of the DSM. The number of GCPs and their position is hard to define a priori, but some simple operative suggestions can be useful for proper distribution of these points: (i) follow the limit of the area of interest, (ii) insert other GCPs inside the area of interest considering also the elevation differences of the area.…”

Comparing sUAS Photogrammetrically-Derived Point Clouds with GNSS Measurements and Terrestrial Laser Scanning for Topographic Mapping

“…The nonmetric camera self-calibration technique was applied to the "uncalibrated" images by manually identifying 5 GCPs on the images and applying them as constraints to refine the camera self-calibration and georeferencing parameters; therefore, georeferencing relied exclusively on GCPs. Ideally, the camera calibration parameters should be estimated in laboratory settings, however, these parameters often change under in-flight conditions [48], therefore, most practitioners prefer to apply the self-calibration method on a flight-to-flight basis [49,50]. Depending on several factors, such as flight configuration (e.g., flying height, overlap, and image orientation), environmental conditions, surface complexity, purpose of sUAS survey, number and distribution of GCPs, quality of GCPs and the flying and shutter speed, the camera self-calibration parameters can vary [51].…”

New Strategies for Time Delay Estimation during System Calibration for UAV-Based GNSS/INS-Assisted Imaging Systems

“…The type of control data used in previous calibration ranges from ground-based surveyed points, on-board GNSS/INS sensors, and superior sensor sources such as LiDAR. The accuracy associated with these types of control data used in calibration is approximately 3 mm for ground-based surveyed points, 8 mm planimetric accuracies for on-board GNSS/INS points, and 25 cm planimetric accuracy for LiDAR-derived control data [30][31][32]. Amongst this work, a variety of calibration parameters are of interest.…”