New Approaches to the Integration of Navigation Systems for Autonomous Unmanned Vehicles (UAV)

Abstract

The article presents an overview of the theoretical and experimental work related to unmanned aerial vehicles (UAVs) motion parameters estimation based on the integration of video measurements obtained by the on-board optoelectronic camera and data from the UAV’s own inertial navigation system (INS). The use of various approaches described in the literature which show good characteristics in computer simulations or in fairly simple conditions close to laboratory ones demonstrates the sufficient complexity of the problems associated with adaption of camera parameters to the changing conditions of a real flight. In our experiments, we used computer simulation methods applying them to the real images and processing methods of videos obtained during real flights. For example, it was noted that the use of images that are very different in scale and in the aspect angle from the observed images in flight makes it very difficult to use the methodology of singular points. At the same time, the matching of the observed and reference images using rectilinear segments, such as images of road sections and the walls of the buildings look quite promising. In addition, in our experiments we used the projective transformation matrix computation from frame to frame, which together with the filtering estimates for the coordinate and angular velocities provides additional possibilities for estimating the UAV position. Data on the UAV position determining based on the methods of video navigation obtained during real flights are presented. New approaches to video navigation obtained using the methods of conjugation rectilinear segments, characteristic curvilinear elements and segmentation of textured and colored regions are demonstrated. Also the application of the method of calculating projective transformations from frame-to-frame is shown which gives estimates of the displacements and rotations of the apparatus and thereby serves to the UAV position estimation by filtering. Thus, the aim of the work was to analyze various approaches to UAV navigation using video data as an additional source of information about the position and velocity of the vehicle.

Unsupervised Human Detection with an Embedded Vision System on a Fully Autonomous UAV for Search and Rescue Operations

“…Issues related to sensing and connectivity of sensors for the assisted or even the autonomous navigation of UAVs, as well as the reliability and the maintenance analysis of such vehicles have been extensively discussed in [30,31]. Moreover, the adaption of camera parameters, as well as motion parameters estimation based on the integration of video measurements and data obtained by UAVs, have always to be seriously taken into account [32]. The relentless motivation for image classification tasks based on deep learning and aerial images captured by UAVs has led to an abundance of research including vehicles [24,33,34], aerial vehicles [35,36,37,38,39], roads [40], buildings [41,42], cracks [43], birds [44], cattle [45], and wilt [46] detection.…”

A Pragmatic Approach to the Design of Advanced Precision Terrain Aided Navigation for UAVs and Its Verification

“…However, the GNSS cannot operate independently as it provides information with outside help and is vulnerable to hostile jamming and spoofing. To overcome such weaknesses, the vision-based navigation technique is commonly used for UAVs [3]- [5], but a problem with the camera adapting to the changing conditions of a real flight is not completely solved yet, so vision-based navigation is mainly used in drone systems that fly at low altitude for a relatively short time [6] and autonomous driving vehicles [7]. As another technique that can resolve the problems of GNSS, the terrain referenced navigation (TRN) can be used.…”

UAV Landing Based on the Optical Flow Videonavigation

“…The UAV control system which includes an optoelectronic system (OES) and an onboard computer determines the movement of the onboard video camera and identifies the objects observed in the field of view of the camera [1]. There are several approaches to the usage of OES [2]. The first one is to detect and to track the movement of specific local areas (reference points) on the image by analogy with human vision [3,4].…”