Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study

Abstract

Personal assistant robots provide novel technological solutions in order to monitor people’s activities, helping them in their daily lives. In this sense, unmanned aerial vehicles (UAVs) can also bring forward a present and future model of assistant robots. To develop aerial assistants, it is necessary to address the issue of autonomous navigation based on visual cues. Indeed, navigating autonomously is still a challenge in which computer vision technologies tend to play an outstanding role. Thus, the design of vision systems and algorithms for autonomous UAV navigation and flight control has become a prominent research field in the last few years. In this paper, a systematic mapping study is carried out in order to obtain a general view of this subject. The study provides an extensive analysis of papers that address computer vision as regards the following autonomous UAV vision-based tasks: (1) navigation, (2) control, (3) tracking or guidance, and (4) sense-and-avoid. The works considered in the mapping study—a total of 144 papers from an initial set of 2081—have been classified under the four categories above. Moreover, type of UAV, features of the vision systems employed and validation procedures are also analyzed. The results obtained make it possible to draw conclusions about the research focuses, which UAV platforms are mostly used in each category, which vision systems are most frequently employed, and which types of tests are usually performed to validate the proposed solutions. The results of this systematic mapping study demonstrate the scientific community’s growing interest in the development of vision-based solutions for autonomous UAVs. Moreover, they will make it possible to study the feasibility and characteristics of future UAVs taking the role of personal assistants.

Method for clustering and identification of objects in laser scanning point clouds using dynamic logic

“…Controlling autonomous ight of UAVs (unmanned aerial vehicles) is a technology close to the above mentioned autonomous driving. It is also important for an autonomous UAV to make quick decisions based on the recognition of landscape objects [34][35][36]. UAVs are often used to create 3D city models based on scanning data [37][38][39].…”

Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologies

“…Specific to a military context, these designed attacks or adversarial patches, when printed, could be used to attack an adversaries' CNN detectors and could act as passive defence against object detectors. This need to camouflage from object detectors is vital as intelligence, surveillance and reconnaissance (ISR) technologies, and fifth generation missiles integrate vision technologies [2], [3], [18], [44], [48]. Traditional camouflage methods are impractical for larger mobile assets, such as aircraft, so adversarial patches should be further developed as an alternative camouflage technique.…”

Fixed Wing Aircraft Automatic Landing with the Use of a Dedicated Ground Sign System

“…An automatic landing requires proper information about the position of the aircraft in relation to the desired landing trajectory, or at the very least to the theoretical touchdown point [15]. This data is usually provided by landing assist systems (e.g., ILS, satellite systems) or could be achieved by utilizing visual signals coming from systems which typically assist pilots of manned aircraft [16][17][18][19][20] as well. In this case, it is obvious that to extract proper information from visual signals, some image processing methods must be employed.…”