# UAV-Based Airport Lights Inspection Without GNSS Positioning Support
## 2 Related Works
Determining light intensity and colour creates the problem of appropriate colour mapping. The common method of determining the camera position is to incorporate GNSS receivers into the UAV. The reading of the satellite data allows us to locate the UAV in world coordinates within a few metres. This is inadequate for the accurate calculation of light positions, so various augmentation methods are used, with the most popular being RTK [19; 20]. RTK is a differential GNSS technique that uses corrections from a base station or reference network. It corrects for errors such as satellite orbit and clock errors, atmospheric delays, and multipath effects. RTK depends on the spatial correlation of these errors between the base station and the receiver. RTK provides high-precision results. The evaluation of light properties may require special processing means as the measurements are performed in noisy environments such as surplus light sources. The authors in [21] proposed to process images in the HSV colour space instead of the usual RGB and listed the advantages of such an approach. Image processing operations such as image edge detection, morphology operations, and the extraction of object features are performed more effectively in a complex airport background. Structure from motion is a candidate for substituting GNSS-based positioning The authors in [22; 23] reviewed the issues of the successful implementation of SfM. The range imaging technique consists of three main actions: the extraction of features in images and matching these features between images; camera motion estimation; and the recovery of the 3D structure using the estimated motion and features. The results presented in [24] indicate that the accuracy of positioning techniques for image acquisition during UAV missions based on RTK, post-processed kinematics (PPK), and precise point positioning--ambiguity resolution (PPP-AR) techniques have a sub-decimeter error value. PPP-AR appears to be an alternative to RTK and PPK, which usually require infrastructure, labour, and higher costs. The fundamental principles and guidelines for developing processing algorithms for these actions are presented in [25]. Based on a sequence of camera images, 3D object coordinates in an arbitrary coordinate system are calculated from the 2D image coordinates of image points. The objects are described using characteristic features tracked in the sequence of images. During this phase, camera parameters, describing the position (three shifts) and orientation (three rotations) at which the images were captured, are estimated. Adding the knowledge about the placement of objects in real-world space coordinates, for calibration, enables the calculation of a corresponding 3D point for almost each image pixel [26, 27]. The solution of the problem of camera position estimation conditions the effectiveness of UAV use for light inspections. UAV flights are susceptible to weather conditions, especially blowing winds that cause the drone to waver. For the estimation of camera positions, a pair-wise direction estimation method immune to outliers in point correspondences between image pairs was proposed in [28]. The process of recovering the scene and the associated camera poses from a set of images is an iterative nonlinear optimization task with respect to 3D feature coordinates and camera poses. The authors in [29] proposed a pose-only imaging geometry framework and algorithms. The representation is a linear function of camera global translations that does not require nonlinear optimization and allows for efficient and robust camera motion estimation.