Multi-sensor field trials for detection and tracking of multiple small unmanned aerial vehicles flying at low altitude

2017 ◽  
Author(s):  
Martin Laurenzis ◽  
Sebastien Hengy ◽  
Alexander Hommes ◽  
Frank Kloeppel ◽  
Alex Shoykhetbrod ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Field Trials ◽  
Detection And Tracking ◽  
Aerial Vehicles ◽  
Sensor Field ◽  
Low Altitude

scholarly journals Efficient Reactive Obstacle Avoidance Using Spirals for Escape

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 51
Author(s):  
Fábio Azevedo ◽  
Jaime S. Cardoso ◽  
André Ferreira ◽  
Tiago Fernandes ◽  
Miguel Moreira ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Mobile Robotics ◽  
Point Clouds ◽  
Depth Camera ◽  
Main Task ◽  
Robust Method ◽  
Aerial Vehicles ◽  
Correct Execution ◽  
Low Altitude ◽  
High Degree

The usage of unmanned aerial vehicles (UAV) has increased in recent years and new application scenarios have emerged. Some of them involve tasks that require a high degree of autonomy, leading to increasingly complex systems. In order for a robot to be autonomous, it requires appropriate perception sensors that interpret the environment and enable the correct execution of the main task of mobile robotics: navigation. In the case of UAVs, flying at low altitude greatly increases the probability of encountering obstacles, so they need a fast, simple, and robust method of collision avoidance. This work covers the problem of navigation in unknown scenarios by implementing a simple, yet robust, environment-reactive approach. The implementation is done with both CPU and GPU map representations to allow wider coverage of possible applications. This method searches for obstacles that cross a cylindrical safety volume, and selects an escape point from a spiral for avoiding the obstacle. The algorithm is able to successfully navigate in complex scenarios, using both a high and low-power computer, typically found aboard UAVs, relying only on a depth camera with a limited FOV and range. Depending on the configuration, the algorithm can process point clouds at nearly 40 Hz in Jetson Nano, while checking for threats at 10 kHz. Some preliminary tests were conducted with real-world scenarios, showing both the advantages and limitations of CPU and GPU-based methodologies.

Radiometric Quality Assessment of Video Sequences Acquired from UAV Photogrammetric Systems

2017 ◽  
Author(s):  
Damian Wierzbicki ◽  
Anna Fryskowska
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Video Stream ◽  
Video Data ◽  
Video Sequences ◽  
Wide Field ◽  
Sensing Applications ◽  
Aerial Vehicles ◽  
High Resolution Imagery ◽  
Low Altitude ◽  
Imagery Data

The issue of imagery data collection and its implementation in photogrammetric studies with the use of unmanned aerial vehicles is still valid and provides a wide field of research in the creation of new and expansion of existing solutions. It is particularly important to increase the accuracy of photogrammetric products. These days low altitude unmanned aerial vehicles are being used more and more often in photogrammetric applications. Compact digital cameras had acquired single, high-resolution imagery. Data obtained from low altitudes were often (and still are) used in mapping and 3D modelling. Due to the low costs of flights of UAV systems in comparison with traditional flights, applications of such platforms are also attractive for many remote sensing applications. However, due to the use of non-metric video cameras, one of the main problems when trying to automate the video data processing, is the video sequences’ relatively poor radiometric quality. The article addresses the issue of assessing the quality of the video imagery acquired from a low altitude UAV platform. The Authors presented quality Indicators dedicated to UAV video sequences. The method is based on the analysis of the video stream, obtained in the different weather and lighting conditions. As a result of the research, an objective quality index for video acquired from low altitudes was determined.

scholarly journals Internet of Unmanned Aerial Vehicles—A Multilayer Low-Altitude Airspace Model for Distributed UAV Traffic Management

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4779 ◽  
Author(s):  
Nader S. Labib ◽  
Grégoire Danoy ◽  
Jedrzej Musial ◽  
Matthias R. Brust ◽  
Pascal Bouvry
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Traffic Management ◽  
Complex Problem ◽  
Experimental Simulation ◽  
Aerial Vehicles ◽  
Safe Management ◽  
Wide Range ◽  
Abstract Level ◽  
Low Altitude ◽  
New Research

The rapid adoption of Internet of Things (IoT) has encouraged the integration of new connected devices such as Unmanned Aerial Vehicles (UAVs) to the ubiquitous network. UAVs promise a pragmatic solution to the limitations of existing terrestrial IoT infrastructure as well as bring new means of delivering IoT services through a wide range of applications. Owning to their potential, UAVs are expected to soon dominate the low-altitude airspace over populated cities. This introduces new research challenges such as the safe management of UAVs operation under high traffic demands. This paper proposes a novel way of structuring the uncontrolled, low-altitude airspace, with the aim of addressing the complex problem of UAV traffic management at an abstract level. The work, hence, introduces a model of the airspace as a weighted multilayer network of nodes and airways and presents a set of experimental simulation results using three UAV traffic management heuristics.

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