5,632 research outputs found
Detection and estimation of moving obstacles for a UAV
In recent years, research interest in Unmanned Aerial Vehicles (UAVs) has been grown rapidly because of their potential use for a wide range of applications. In this paper, we proposed a vision-based detection and position/velocity estimation of moving obstacle for a UAV. The knowledge of a moving obstacle's state, i.e., position, velocity, is essential to achieve better performance for an intelligent UAV system specially in autonomous navigation and landing tasks. The novelties are: (1) the design and implementation of a localization method using sensor fusion methodology which fuses Inertial Measurement Unit (IMU) signals and Pozyx signals; (2) The development of detection and estimation of moving obstacles method based on on-board vision system. Experimental results validate the effectiveness of the proposed approach. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved
Detecting Invasive Insects with Unmanned Aerial Vehicles
A key aspect to controlling and reducing the effects invasive insect species
have on agriculture is to obtain knowledge about the migration patterns of
these species. Current state-of-the-art methods of studying these migration
patterns involve a mark-release-recapture technique, in which insects are
released after being marked and researchers attempt to recapture them later.
However, this approach involves a human researcher manually searching for these
insects in large fields and results in very low recapture rates. In this paper,
we propose an automated system for detecting released insects using an unmanned
aerial vehicle. This system utilizes ultraviolet lighting technology, digital
cameras, and lightweight computer vision algorithms to more quickly and
accurately detect insects compared to the current state of the art. The
efficiency and accuracy that this system provides will allow for a more
comprehensive understanding of invasive insect species migration patterns. Our
experimental results demonstrate that our system can detect real target insects
in field conditions with high precision and recall rates.Comment: IEEE ICRA 2019. 7 page
Ground Profile Recovery from Aerial 3D LiDAR-based Maps
The paper presents the study and implementation of the ground detection
methodology with filtration and removal of forest points from LiDAR-based 3D
point cloud using the Cloth Simulation Filtering (CSF) algorithm. The
methodology allows to recover a terrestrial relief and create a landscape map
of a forestry region. As the proof-of-concept, we provided the outdoor flight
experiment, launching a hexacopter under a mixed forestry region with sharp
ground changes nearby Innopolis city (Russia), which demonstrated the
encouraging results for both ground detection and methodology robustness.Comment: 8 pages, FRUCT-2019 conferenc
Unmanned Aerial Systems for Wildland and Forest Fires
Wildfires represent an important natural risk causing economic losses, human
death and important environmental damage. In recent years, we witness an
increase in fire intensity and frequency. Research has been conducted towards
the development of dedicated solutions for wildland and forest fire assistance
and fighting. Systems were proposed for the remote detection and tracking of
fires. These systems have shown improvements in the area of efficient data
collection and fire characterization within small scale environments. However,
wildfires cover large areas making some of the proposed ground-based systems
unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial
Systems (UAS) were proposed. UAS have proven to be useful due to their
maneuverability, allowing for the implementation of remote sensing, allocation
strategies and task planning. They can provide a low-cost alternative for the
prevention, detection and real-time support of firefighting. In this paper we
review previous work related to the use of UAS in wildfires. Onboard sensor
instruments, fire perception algorithms and coordination strategies are
considered. In addition, we present some of the recent frameworks proposing the
use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more
efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at:
https://doi.org/10.3390/drones501001
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