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17071 KiB

Open AccessArticle

Atmospheric Sampling on Ascension Island Using Multirotor UAVs

by Colin Greatwood, Thomas S. Richardson, Jim Freer, Rick M. Thomas, A. Rob MacKenzie, Rebecca Brownlow, David Lowry, Rebecca E. Fisher and Euan G. Nisbet

Sensors 2017, 17(6), 1189; https://doi.org/10.3390/s17061189 - 23 May 2017

Cited by 30 | Viewed by 7363

Abstract

As part of an NERC-funded project investigating the southern methane anomaly, a team drawn from the Universities of Bristol, Birmingham and Royal Holloway flew small unmanned multirotors from Ascension Island for the purposes of atmospheric sampling. The objective of these flights was to [...] Read more.

As part of an NERC-funded project investigating the southern methane anomaly, a team drawn from the Universities of Bristol, Birmingham and Royal Holloway flew small unmanned multirotors from Ascension Island for the purposes of atmospheric sampling. The objective of these flights was to collect air samples from below, within and above a persistent atmospheric feature, the Trade Wind Inversion, in order to characterise methane concentrations and their isotopic composition. These parameters allow the methane in the different air masses to be tied to different source locations, which can be further analysed using back trajectory atmospheric computer modelling. This paper describes the campaigns as a whole including the design of the bespoke eight rotor aircraft and the operational requirements that were needed in order to collect targeted multiple air samples up to 2.5 km above the ground level in under 20 min of flight time. Key features of the system described include real-time feedback of temperature and humidity, as well as system health data. This enabled detailed targeting of the air sampling design to be realised and planned during the flight mission on the downward leg, a capability that is invaluable in the presence of uncertainty in the pre-flight meteorological data. Environmental considerations are also outlined together with the flight plans that were created in order to rapidly fly vertical transects of the atmosphere whilst encountering changing wind conditions. Two sampling campaigns were carried out in September 2014 and July 2015 with over one hundred high altitude sampling missions. Lessons learned are given throughout, including those associated with operating in the testing environment encountered on Ascension Island. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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14072 KiB

Open AccessArticle

Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs

by Abdulla Al-Kaff, Fernando García, David Martín, Arturo De La Escalera and José María Armingol

Sensors 2017, 17(5), 1061; https://doi.org/10.3390/s17051061 - 07 May 2017

Cited by 82 | Viewed by 11932

Abstract

One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, [...] Read more.

One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar.For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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5240 KiB

Open AccessArticle

Coastal Areas Division and Coverage with Multiple UAVs for Remote Sensing

by Fotios Balampanis, Iván Maza and Aníbal Ollero

Sensors 2017, 17(4), 808; https://doi.org/10.3390/s17040808 - 09 Apr 2017

Cited by 53 | Viewed by 6002

Abstract

This paper tackles the problems of exact cell decomposition and partitioning of a coastal region for a team of heterogeneous Unmanned Aerial Vehicles (UAVs) with an approach that takes into account the field of view or sensing radius of the sensors on-board. An [...] Read more.

This paper tackles the problems of exact cell decomposition and partitioning of a coastal region for a team of heterogeneous Unmanned Aerial Vehicles (UAVs) with an approach that takes into account the field of view or sensing radius of the sensors on-board. An initial sensor-based exact cell decomposition of the area aids in the partitioning process, which is performed in two steps. In the first step, a growing regions algorithm performs an isotropic partitioning of the area based on the initial locations of the UAVs and their relative capabilities. Then, two novel algorithms are applied to compute an adjustment of this partitioning process, in order to solve deadlock situations that generate non-allocated regions and sub-areas above or below the relative capabilities of the UAVs. Finally, realistic simulations have been conducted for the evaluation of the proposed solution, and the obtained results show that these algorithms can compute valid and sound solutions in complex coastal region scenarios under different setups for the UAVs. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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9104 KiB

Open AccessArticle

Time-of-Travel Methods for Measuring Optical Flow on Board a Micro Flying Robot

by Erik Vanhoutte, Stefano Mafrica, Franck Ruffier, Reinoud J. Bootsma and Julien Serres

Sensors 2017, 17(3), 571; https://doi.org/10.3390/s17030571 - 11 Mar 2017

Cited by 18 | Viewed by 5670

Abstract

For use in autonomous micro air vehicles, visual sensors must not only be small, lightweight and insensitive to light variations; on-board autopilots also require fast and accurate optical flow measurements over a wide range of speeds. Using an auto-adaptive bio-inspired Michaelis–Menten Auto-adaptive Pixel [...] Read more.

For use in autonomous micro air vehicles, visual sensors must not only be small, lightweight and insensitive to light variations; on-board autopilots also require fast and accurate optical flow measurements over a wide range of speeds. Using an auto-adaptive bio-inspired Michaelis–Menten Auto-adaptive Pixel (M

^{2}

APix) analog silicon retina, in this article, we present comparative tests of two optical flow calculation algorithms operating under lighting conditions from

6 \times 10^{- 7}

to

1.6 \times 10^{- 2}

W·cm

^{- 2}

(i.e., from 0.2 to 12,000 lux for human vision). Contrast “time of travel” between two adjacent light-sensitive pixels was determined by thresholding and by cross-correlating the two pixels’ signals, with measurement frequency up to 5 kHz for the 10 local motion sensors of the M

^{2}

APix sensor. While both algorithms adequately measured optical flow between 25

^{\circ}

/s and 1000

^{\circ}

/s, thresholding gave rise to a lower precision, especially due to a larger number of outliers at higher speeds. Compared to thresholding, cross-correlation also allowed for a higher rate of optical flow output (99 Hz and 1195 Hz, respectively) but required substantially more computational resources. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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9174 KiB

Open AccessArticle

A Novel System for Correction of Relative Angular Displacement between Airborne Platform and UAV in Target Localization

by Chenglong Liu, Jinghong Liu, Yueming Song and Huaidan Liang

Sensors 2017, 17(3), 510; https://doi.org/10.3390/s17030510 - 04 Mar 2017

Cited by 19 | Viewed by 5193 | Correction

Abstract

This paper provides a system and method for correction of relative angular displacements between an Unmanned Aerial Vehicle (UAV) and its onboard strap-down photoelectric platform to improve localization accuracy. Because the angular displacements have an influence on the final accuracy, by attaching a [...] Read more.

This paper provides a system and method for correction of relative angular displacements between an Unmanned Aerial Vehicle (UAV) and its onboard strap-down photoelectric platform to improve localization accuracy. Because the angular displacements have an influence on the final accuracy, by attaching a measuring system to the platform, the texture image of platform base bulkhead can be collected in a real-time manner. Through the image registration, the displacement vector of the platform relative to its bulkhead can be calculated to further determine angular displacements. After being decomposed and superposed on the three attitude angles of the UAV, the angular displacements can reduce the coordinate transformation errors and thus improve the localization accuracy. Even a simple kind of method can improve the localization accuracy by 14.3%. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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5339 KiB

Open AccessArticle

Development of an Unmanned Aerial Vehicle-Borne Crop-Growth Monitoring System

by Jun Ni, Lili Yao, Jingchao Zhang, Weixing Cao, Yan Zhu and Xiuxiang Tai

Sensors 2017, 17(3), 502; https://doi.org/10.3390/s17030502 - 03 Mar 2017

Cited by 38 | Viewed by 7232

Abstract

In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online [...] Read more.

In view of the demand for a low-cost, high-throughput method for the continuous acquisition of crop growth information, this study describes a crop-growth monitoring system which uses an unmanned aerial vehicle (UAV) as an operating platform. The system is capable of real-time online acquisition of various major indexes, e.g., the normalized difference vegetation index (NDVI) of the crop canopy, ratio vegetation index (RVI), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW). By carrying out three-dimensional numerical simulations based on computational fluid dynamics, spatial distributions were obtained for the UAV down-wash flow fields on the surface of the crop canopy. Based on the flow-field characteristics and geometrical dimensions, a UAV-borne crop-growth sensor was designed. Our field experiments show that the monitoring system has good dynamic stability and measurement accuracy over the range of operating altitudes of the sensor. The linear fitting determination coefficients (R²) for the output RVI value with respect to LNA, LAI, and LDW are 0.63, 0.69, and 0.66, respectively, and the Root-mean-square errors (RMSEs) are 1.42, 1.02 and 3.09, respectively. The equivalent figures for the output NDVI value are 0.60, 0.65, and 0.62 (LNA, LAI, and LDW, respectively) and the RMSEs are 1.44, 1.01 and 3.01, respectively. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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3258 KiB

Open AccessArticle

Coordinated Target Tracking via a Hybrid Optimization Approach

by Yin Wang and Yan Cao

Sensors 2017, 17(3), 472; https://doi.org/10.3390/s17030472 - 27 Feb 2017

Cited by 4 | Viewed by 4350

Abstract

Recent advances in computer science and electronics have greatly expanded the capabilities of unmanned aerial vehicles (UAV) in both defense and civil applications, such as moving ground object tracking. Due to the uncertainties of the application environments and objects’ motion, it is difficult [...] Read more.

Recent advances in computer science and electronics have greatly expanded the capabilities of unmanned aerial vehicles (UAV) in both defense and civil applications, such as moving ground object tracking. Due to the uncertainties of the application environments and objects’ motion, it is difficult to maintain the tracked object always within the sensor coverage area by using a single UAV. Hence, it is necessary to deploy a group of UAVs to improve the robustness of the tracking. This paper investigates the problem of tracking ground moving objects with a group of UAVs using gimbaled sensors under flight dynamic and collision-free constraints. The optimal cooperative tracking path planning problem is solved using an evolutionary optimization technique based on the framework of chemical reaction optimization (CRO). The efficiency of the proposed method was demonstrated through a series of comparative simulations. The results show that the cooperative tracking paths determined by the newly developed method allows for longer sensor coverage time under flight dynamic restrictions and safety conditions. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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6194 KiB

Open AccessArticle

Unmanned Aerial Vehicle Systems for Remote Estimation of Flooded Areas Based on Complex Image Processing

by Dan Popescu, Loretta Ichim and Florin Stoican

Sensors 2017, 17(3), 446; https://doi.org/10.3390/s17030446 - 23 Feb 2017

Cited by 47 | Viewed by 6418

Abstract

Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex [...] Read more.

Floods are natural disasters which cause the most economic damage at the global level. Therefore, flood monitoring and damage estimation are very important for the population, authorities and insurance companies. The paper proposes an original solution, based on a hybrid network and complex image processing, to this problem. As first novelty, a multilevel system, with two components, terrestrial and aerial, was proposed and designed by the authors as support for image acquisition from a delimited region. The terrestrial component contains a Ground Control Station, as a coordinator at distance, which communicates via the internet with more Ground Data Terminals, as a fixed nodes network for data acquisition and communication. The aerial component contains mobile nodes—fixed wing type UAVs. In order to evaluate flood damage, two tasks must be accomplished by the network: area coverage and image processing. The second novelty of the paper consists of texture analysis in a deep neural network, taking into account new criteria for feature selection and patch classification. Color and spatial information extracted from chromatic co-occurrence matrix and mass fractal dimension were used as well. Finally, the experimental results in a real mission demonstrate the validity of the proposed methodologies and the performances of the algorithms. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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5114 KiB

Open AccessArticle

Gimbal Influence on the Stability of Exterior Orientation Parameters of UAV Acquired Images

by Mateo Gašparović and Luka Jurjević

Sensors 2017, 17(2), 401; https://doi.org/10.3390/s17020401 - 18 Feb 2017

Cited by 40 | Viewed by 11254

Abstract

In this paper, results from the analysis of the gimbal impact on the determination of the camera exterior orientation parameters of an Unmanned Aerial Vehicle (UAV) are presented and interpreted. Additionally, a new approach and methodology for testing the influence of gimbals on [...] Read more.

In this paper, results from the analysis of the gimbal impact on the determination of the camera exterior orientation parameters of an Unmanned Aerial Vehicle (UAV) are presented and interpreted. Additionally, a new approach and methodology for testing the influence of gimbals on the exterior orientation parameters of UAV acquired images is presented. The main motive of this study is to examine the possibility of obtaining better geometry and favorable spatial bundles of rays of images in UAV photogrammetric surveying. The subject is a 3-axis brushless gimbal based on a controller board (Storm32). Only two gimbal axes are taken into consideration: roll and pitch axes. Testing was done in a flight simulation, and in indoor and outdoor flight mode, to analyze the Inertial Measurement Unit (IMU) and photogrammetric data. Within these tests the change of the exterior orientation parameters without the use of a gimbal is determined, as well as the potential accuracy of the stabilization with the use of a gimbal. The results show that using a gimbal has huge potential. Significantly, smaller discrepancies between data are noticed when a gimbal is used in flight simulation mode, even four times smaller than in other test modes. In this test the potential accuracy of a low budget gimbal for application in real conditions is determined. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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14195 KiB

Open AccessArticle

A Methodology to Monitor Airborne PM₁₀ Dust Particles Using a Small Unmanned Aerial Vehicle

by Miguel Alvarado, Felipe Gonzalez, Peter Erskine, David Cliff and Darlene Heuff

Sensors 2017, 17(2), 343; https://doi.org/10.3390/s17020343 - 14 Feb 2017

Cited by 54 | Viewed by 9315

Abstract

Throughout the process of coal extraction from surface mines, gases and particles are emitted in the form of fugitive emissions by activities such as hauling, blasting and transportation. As these emissions are diffuse in nature, estimations based upon emission factors and dispersion/advection equations [...] Read more.

Throughout the process of coal extraction from surface mines, gases and particles are emitted in the form of fugitive emissions by activities such as hauling, blasting and transportation. As these emissions are diffuse in nature, estimations based upon emission factors and dispersion/advection equations need to be measured directly from the atmosphere. This paper expands upon previous research undertaken to develop a relative methodology to monitor PM₁₀ dust particles produced by mining activities making use of small unmanned aerial vehicles (UAVs). A module sensor using a laser particle counter (OPC-N2 from Alphasense, Great Notley, Essex, UK) was tested. An aerodynamic flow experiment was undertaken to determine the position and length of a sampling probe of the sensing module. Flight tests were conducted in order to demonstrate that the sensor provided data which could be used to calculate the emission rate of a source. Emission rates are a critical variable for further predictive dispersion estimates. First, data collected by the airborne module was verified using a 5.0 m tower in which a TSI DRX 8533 (reference dust monitoring device, TSI, Shoreview, MN, USA) and a duplicate of the module sensor were installed. Second, concentration values collected by the monitoring module attached to the UAV (airborne module) obtaining a percentage error of 1.1%. Finally, emission rates from the source were calculated, with airborne data, obtaining errors as low as 1.2%. These errors are low and indicate that the readings collected with the airborne module are comparable to the TSI DRX and could be used to obtain specific emission factors from fugitive emissions for industrial activities. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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3612 KiB

Open AccessArticle

Water Plume Temperature Measurements by an Unmanned Aerial System (UAS)

by Anthony DeMario, Pete Lopez, Eli Plewka, Ryan Wix, Hai Xia, Emily Zamora, Dan Gessler and Azer P. Yalin

Sensors 2017, 17(2), 306; https://doi.org/10.3390/s17020306 - 07 Feb 2017

Cited by 18 | Viewed by 5204

Abstract

We report on the development and testing of a proof of principle water temperature measurement system deployed on an unmanned aerial system (UAS), for field measurements of thermal discharges into water. The primary elements of the system include a quad-copter UAS to which [...] Read more.

We report on the development and testing of a proof of principle water temperature measurement system deployed on an unmanned aerial system (UAS), for field measurements of thermal discharges into water. The primary elements of the system include a quad-copter UAS to which has been integrated, for the first time, both a thermal imaging infrared (IR) camera and an immersible probe that can be dipped below the water surface to obtain vertical water temperature profiles. The IR camera is used to take images of the overall water surface to geo-locate the plume, while the immersible probe provides quantitative temperature depth profiles at specific locations. The full system has been tested including the navigation of the UAS, its ability to safely carry the sensor payload, and the performance of both the IR camera and the temperature probe. Finally, the UAS sensor system was successfully deployed in a pilot field study at a coal burning power plant, and obtained images and temperature profiles of the thermal effluent. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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11160 KiB

Open AccessArticle

Augmented Reality Tool for the Situational Awareness Improvement of UAV Operators

by Susana Ruano, Carlos Cuevas, Guillermo Gallego and Narciso García

Sensors 2017, 17(2), 297; https://doi.org/10.3390/s17020297 - 06 Feb 2017

Cited by 20 | Viewed by 9012

Abstract

Unmanned Aerial Vehicles (UAVs) are being extensively used nowadays. Therefore, pilots of traditional aerial platforms should adapt their skills to operate them from a Ground Control Station (GCS). Common GCSs provide information in separate screens: one presents the video stream while the other [...] Read more.

Unmanned Aerial Vehicles (UAVs) are being extensively used nowadays. Therefore, pilots of traditional aerial platforms should adapt their skills to operate them from a Ground Control Station (GCS). Common GCSs provide information in separate screens: one presents the video stream while the other displays information about the mission plan and information coming from other sensors. To avoid the burden of fusing information displayed in the two screens, an Augmented Reality (AR) tool is proposed in this paper. The AR system has two functionalities for Medium-Altitude Long-Endurance (MALE) UAVs: route orientation and target identification. Route orientation allows the operator to identify the upcoming waypoints and the path that the UAV is going to follow. Target identification allows a fast target localization, even in the presence of occlusions. The AR tool is implemented following the North Atlantic Treaty Organization (NATO) standards so that it can be used in different GCSs. The experiments show how the AR tool improves significantly the situational awareness of the UAV operators. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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6767 KiB

Open AccessArticle

A Hierarchical Building Segmentation in Digital Surface Models for 3D Reconstruction

by Yiming Yan, Fengjiao Gao, Shupei Deng and Nan Su

Sensors 2017, 17(2), 222; https://doi.org/10.3390/s17020222 - 24 Jan 2017

Cited by 17 | Viewed by 5907

Abstract

In this study, a hierarchical method for segmenting buildings in a digital surface model (DSM), which is used in a novel framework for 3D reconstruction, is proposed. Most 3D reconstructions of buildings are model-based. However, the limitations of these methods are overreliance on [...] Read more.

In this study, a hierarchical method for segmenting buildings in a digital surface model (DSM), which is used in a novel framework for 3D reconstruction, is proposed. Most 3D reconstructions of buildings are model-based. However, the limitations of these methods are overreliance on completeness of the offline-constructed models of buildings, and the completeness is not easily guaranteed since in modern cities buildings can be of a variety of types. Therefore, a model-free framework using high precision DSM and texture-images buildings was introduced. There are two key problems with this framework. The first one is how to accurately extract the buildings from the DSM. Most segmentation methods are limited by either the terrain factors or the difficult choice of parameter-settings. A level-set method are employed to roughly find the building regions in the DSM, and then a recently proposed ‘occlusions of random textures model’ are used to enhance the local segmentation of the buildings. The second problem is how to generate the facades of buildings. Synergizing with the corresponding texture-images, we propose a roof-contour guided interpolation of building facades. The 3D reconstruction results achieved by airborne-like images and satellites are compared. Experiments show that the segmentation method has good performance, and 3D reconstruction is easily performed by our framework, and better visualization results can be obtained by airborne-like images, which can be further replaced by UAV images. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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19859 KiB

Open AccessArticle

Detection, Location and Grasping Objects Using a Stereo Sensor on UAV in Outdoor Environments

by Pablo Ramon Soria, Begoña C. Arrue and Anibal Ollero

Sensors 2017, 17(1), 103; https://doi.org/10.3390/s17010103 - 07 Jan 2017

Cited by 49 | Viewed by 8026

Abstract

The article presents a vision system for the autonomous grasping of objects with Unmanned Aerial Vehicles (UAVs) in real time. Giving UAVs the capability to manipulate objects vastly extends their applications, as they are capable of accessing places that are difficult to reach [...] Read more.

The article presents a vision system for the autonomous grasping of objects with Unmanned Aerial Vehicles (UAVs) in real time. Giving UAVs the capability to manipulate objects vastly extends their applications, as they are capable of accessing places that are difficult to reach or even unreachable for human beings. This work is focused on the grasping of known objects based on feature models. The system runs in an on-board computer on a UAV equipped with a stereo camera and a robotic arm. The algorithm learns a feature-based model in an offline stage, then it is used online for detection of the targeted object and estimation of its position. This feature-based model was proved to be robust to both occlusions and the presence of outliers. The use of stereo cameras improves the learning stage, providing 3D information and helping to filter features in the online stage. An experimental system was derived using a rotary-wing UAV and a small manipulator for final proof of concept. The robotic arm is designed with three degrees of freedom and is lightweight due to payload limitations of the UAV. The system has been validated with different objects, both indoors and outdoors. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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5127 KiB

Open AccessArticle

Two-UAV Intersection Localization System Based on the Airborne Optoelectronic Platform

by Guanbing Bai, Jinghong Liu, Yueming Song and Yujia Zuo

Sensors 2017, 17(1), 98; https://doi.org/10.3390/s17010098 - 06 Jan 2017

Cited by 33 | Viewed by 6129

Abstract

To address the limitation of the existing UAV (unmanned aerial vehicles) photoelectric localization method used for moving objects, this paper proposes an improved two-UAV intersection localization system based on airborne optoelectronic platforms by using the crossed-angle localization method of photoelectric theodolites for reference. [...] Read more.

To address the limitation of the existing UAV (unmanned aerial vehicles) photoelectric localization method used for moving objects, this paper proposes an improved two-UAV intersection localization system based on airborne optoelectronic platforms by using the crossed-angle localization method of photoelectric theodolites for reference. This paper introduces the makeup and operating principle of intersection localization system, creates auxiliary coordinate systems, transforms the LOS (line of sight, from the UAV to the target) vectors into homogeneous coordinates, and establishes a two-UAV intersection localization model. In this paper, the influence of the positional relationship between UAVs and the target on localization accuracy has been studied in detail to obtain an ideal measuring position and the optimal localization position where the optimal intersection angle is 72.6318°. The result shows that, given the optimal position, the localization root mean square error (RMS) will be 25.0235 m when the target is 5 km away from UAV baselines. Finally, the influence of modified adaptive Kalman filtering on localization results is analyzed, and an appropriate filtering model is established to reduce the localization RMS error to 15.7983 m. Finally, An outfield experiment was carried out and obtained the optimal results:

σ_{B} = 1.63 \times 10^{- 4} (°)

,

σ_{L} = 1.35 \times 10^{- 4} (°)

,

σ_{H} = 15.8 (m)

,

σ_{s u m} = 27.6 (m)

, where

σ_{B}

represents the longitude error,

σ_{L}

represents the latitude error,

σ_{H}

represents the altitude error, and

σ_{s u m}

represents the error radius. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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9934 KiB

Open AccessArticle

Real-Time Multi-Target Localization from Unmanned Aerial Vehicles

by Xuan Wang, Jinghong Liu and Qianfei Zhou

Sensors 2017, 17(1), 33; https://doi.org/10.3390/s17010033 - 25 Dec 2016

Cited by 50 | Viewed by 9109

Abstract

In order to improve the reconnaissance efficiency of unmanned aerial vehicle (UAV) electro-optical stabilized imaging systems, a real-time multi-target localization scheme based on an UAV electro-optical stabilized imaging system is proposed. First, a target location model is studied. Then, the geodetic coordinates of [...] Read more.

In order to improve the reconnaissance efficiency of unmanned aerial vehicle (UAV) electro-optical stabilized imaging systems, a real-time multi-target localization scheme based on an UAV electro-optical stabilized imaging system is proposed. First, a target location model is studied. Then, the geodetic coordinates of multi-targets are calculated using the homogeneous coordinate transformation. On the basis of this, two methods which can improve the accuracy of the multi-target localization are proposed: (1) the real-time zoom lens distortion correction method; (2) a recursive least squares (RLS) filtering method based on UAV dead reckoning. The multi-target localization error model is established using Monte Carlo theory. In an actual flight, the UAV flight altitude is 1140 m. The multi-target localization results are within the range of allowable error. After we use a lens distortion correction method in a single image, the circular error probability (CEP) of the multi-target localization is reduced by 7%, and 50 targets can be located at the same time. The RLS algorithm can adaptively estimate the location data based on multiple images. Compared with multi-target localization based on a single image, CEP of the multi-target localization using RLS is reduced by 25%. The proposed method can be implemented on a small circuit board to operate in real time. This research is expected to significantly benefit small UAVs which need multi-target geo-location functions. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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1850 KiB

Open AccessArticle

Small UAS-Based Wind Feature Identification System Part 1: Integration and Validation

by Leopoldo Rodriguez Salazar, Jose A. Cobano and Anibal Ollero

Sensors 2017, 17(1), 8; https://doi.org/10.3390/s17010008 - 23 Dec 2016

Cited by 23 | Viewed by 7395

Abstract

This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a [...] Read more.

This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1

Hz

and a wind map can be updated at 0.4

Hz

. Predictions show a convergence time with a 95% confidence interval of approximately 30

s

. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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3853 KiB

Open AccessArticle

Development and Validation of a UAV Based System for Air Pollution Measurements

by Tommaso Francesco Villa, Farhad Salimi, Kye Morton, Lidia Morawska and Felipe Gonzalez

Sensors 2016, 16(12), 2202; https://doi.org/10.3390/s16122202 - 21 Dec 2016

Cited by 151 | Viewed by 13405

Abstract

Air quality data collection near pollution sources is difficult, particularly when sites are complex, have physical barriers, or are themselves moving. Small Unmanned Aerial Vehicles (UAVs) offer new approaches to air pollution and atmospheric studies. However, there are a number of critical design [...] Read more.

Air quality data collection near pollution sources is difficult, particularly when sites are complex, have physical barriers, or are themselves moving. Small Unmanned Aerial Vehicles (UAVs) offer new approaches to air pollution and atmospheric studies. However, there are a number of critical design decisions which need to be made to enable representative data collection, in particular the location of the air sampler or air sensor intake. The aim of this research was to establish the best mounting point for four gas sensors and a Particle Number Concentration (PNC) monitor, onboard a hexacopter, so to develop a UAV system capable of measuring point source emissions. The research included two different tests: (1) evaluate the air flow behavior of a hexacopter, its downwash and upwash effect, by measuring air speed along three axes to determine the location where the sensors should be mounted; (2) evaluate the use of gas sensors for CO₂, CO, NO₂ and NO, and the PNC monitor (DISCmini) to assess the efficiency and performance of the UAV based system by measuring emissions from a diesel engine. The air speed behavior map produced by test 1 shows the best mounting point for the sensors to be alongside the UAV. This position is less affected by the propeller downwash effect. Test 2 results demonstrated that the UAV propellers cause a dispersion effect shown by the decrease of gas and PN concentration measured in real time. A Linear Regression model was used to estimate how the sensor position, relative to the UAV center, affects pollutant concentration measurements when the propellers are turned on. This research establishes guidelines on how to develop a UAV system to measure point source emissions. Such research should be undertaken before any UAV system is developed for real world data collection. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

Diverse Planning for UAV Control and Remote Sensing

by Jan Tožička and Antonín Komenda

Sensors 2016, 16(12), 2199; https://doi.org/10.3390/s16122199 - 21 Dec 2016

Cited by 3 | Viewed by 5634

Abstract

Unmanned aerial vehicles (UAVs) are suited to various remote sensing missions, such as measuring air quality. The conventional method of UAV control is by human operators. Such an approach is limited by the ability of cooperation among the operators controlling larger fleets of [...] Read more.

Unmanned aerial vehicles (UAVs) are suited to various remote sensing missions, such as measuring air quality. The conventional method of UAV control is by human operators. Such an approach is limited by the ability of cooperation among the operators controlling larger fleets of UAVs in a shared area. The remedy for this is to increase autonomy of the UAVs in planning their trajectories by considering other UAVs and their plans. To provide such improvement in autonomy, we need better algorithms for generating alternative trajectory variants that the UAV coordination algorithms can utilize. In this article, we define a novel family of multi-UAV sensing problems, solving task allocation of huge number of tasks (tens of thousands) to a group of configurable UAVs with non-zero weight of equipped sensors (comprising the air quality measurement as well) together with two base-line solvers. To solve the problem efficiently, we use an algorithm for diverse trajectory generation and integrate it with a solver for the multi-UAV coordination problem. Finally, we experimentally evaluate the multi-UAV sensing problem solver. The evaluation is done on synthetic and real-world-inspired benchmarks in a multi-UAV simulator. Results show that diverse planning is a valuable method for remote sensing applications containing multiple UAVs. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

Autonomous Aeromagnetic Surveys Using a Fluxgate Magnetometer

by Douglas G. Macharet, Héctor I. A. Perez-Imaz, Paulo A. F. Rezeck, Guilherme A. Potje, Luiz C. C. Benyosef, André Wiermann, Gustavo M. Freitas, Luis G. U. Garcia and Mario F. M. Campos

Sensors 2016, 16(12), 2169; https://doi.org/10.3390/s16122169 - 17 Dec 2016

Cited by 31 | Viewed by 11198

Abstract

Recent advances in the research of autonomous vehicles have showed a vast range of applications, such as exploration, surveillance and environmental monitoring. Considering the mining industry, it is possible to use such vehicles in the prospection of minerals of commercial interest beneath the [...] Read more.

Recent advances in the research of autonomous vehicles have showed a vast range of applications, such as exploration, surveillance and environmental monitoring. Considering the mining industry, it is possible to use such vehicles in the prospection of minerals of commercial interest beneath the ground. However, tasks such as geophysical surveys are highly dependent on specific sensors, which mostly are not designed to be used in these new range of autonomous vehicles. In this work, we propose a novel magnetic survey pipeline that aims to increase versatility, speed and robustness by using autonomous rotary-wing Unmanned Aerial Vehicles (UAVs). We also discuss the development of a state-of-the-art three-axis fluxgate, where our goal in this work was to refine and adjust the sensor topology and coupled electronics specifically for this type of vehicle and application. The sensor was built with two ring-cores using a specially developed stress-annealed CoFeSiB amorphous ribbon, in order to get sufficient resolution to detect concentrations of small ferrous minerals. Finally, we report on the results of experiments performed with a real UAV in an outdoor environment, showing the efficacy of the methodology in detecting an artificial ferrous anomaly. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

Differential GNSS and Vision-Based Tracking to Improve Navigation Performance in Cooperative Multi-UAV Systems

by Amedeo Rodi Vetrella, Giancarmine Fasano, Domenico Accardo and Antonio Moccia

Sensors 2016, 16(12), 2164; https://doi.org/10.3390/s16122164 - 17 Dec 2016

Cited by 62 | Viewed by 8602

Abstract

Autonomous navigation of micro-UAVs is typically based on the integration of low cost Global Navigation Satellite System (GNSS) receivers and Micro-Electro-Mechanical Systems (MEMS)-based inertial and magnetic sensors to stabilize and control the flight. The resulting navigation performance in terms of position and attitude [...] Read more.

Autonomous navigation of micro-UAVs is typically based on the integration of low cost Global Navigation Satellite System (GNSS) receivers and Micro-Electro-Mechanical Systems (MEMS)-based inertial and magnetic sensors to stabilize and control the flight. The resulting navigation performance in terms of position and attitude accuracy may not suffice for other mission needs, such as the ones relevant to fine sensor pointing. In this framework, this paper presents a cooperative UAV navigation algorithm that allows a chief vehicle, equipped with inertial and magnetic sensors, a Global Positioning System (GPS) receiver, and a vision system, to improve its navigation performance (in real time or in the post processing phase) exploiting formation flying deputy vehicles equipped with GPS receivers. The focus is set on outdoor environments and the key concept is to exploit differential GPS among vehicles and vision-based tracking (DGPS/Vision) to build a virtual additional navigation sensor whose information is then integrated in a sensor fusion algorithm based on an Extended Kalman Filter. The developed concept and processing architecture are described, with a focus on DGPS/Vision attitude determination algorithm. Performance assessment is carried out on the basis of both numerical simulations and flight tests. In the latter ones, navigation estimates derived from the DGPS/Vision approach are compared with those provided by the onboard autopilot system of a customized quadrotor. The analysis shows the potential of the developed approach, mainly deriving from the possibility to exploit magnetic- and inertial-independent accurate attitude information. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

Cramer-Rao Lower Bound Evaluation for Linear Frequency Modulation Based Active Radar Networks Operating in a Rice Fading Environment

by Chenguang Shi, Sana Salous, Fei Wang and Jianjiang Zhou

Sensors 2016, 16(12), 2072; https://doi.org/10.3390/s16122072 - 06 Dec 2016

Cited by 10 | Viewed by 6250

Abstract

This paper investigates the joint target parameter (delay and Doppler) estimation performance of linear frequency modulation (LFM)-based radar networks in a Rice fading environment. The active radar networks are composed of multiple radar transmitters and multichannel receivers placed on moving platforms. First, the [...] Read more.

This paper investigates the joint target parameter (delay and Doppler) estimation performance of linear frequency modulation (LFM)-based radar networks in a Rice fading environment. The active radar networks are composed of multiple radar transmitters and multichannel receivers placed on moving platforms. First, the log-likelihood function of the received signal for a Rician target is derived, where the received signal scattered off the target comprises of dominant scatterer (DS) component and weak isotropic scatterers (WIS) components. Then, the analytically closed-form expressions of the Cramer-Rao lower bounds (CRLBs) on the Cartesian coordinates of target position and velocity are calculated, which can be adopted as a performance metric to access the target parameter estimation accuracy for LFM-based radar network systems in a Rice fading environment. It is found that the cumulative Fisher information matrix (FIM) is a linear combination of both DS component and WIS components, and it also demonstrates that the joint CRLB is a function of signal-to-noise ratio (SNR), target’s radar cross section (RCS) and transmitted waveform parameters, as well as the relative geometry between the target and the radar network architectures. Finally, numerical results are provided to indicate that the joint target parameter estimation performance of active radar networks can be significantly improved with the exploitation of DS component. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

A Novel Method for Vertical Acceleration Noise Suppression of a Thrust-Vectored VTOL UAV

by Huanyu Li, Linfeng Wu, Yingjie Li, Chunwen Li and Hangyu Li

Sensors 2016, 16(12), 2054; https://doi.org/10.3390/s16122054 - 02 Dec 2016

Cited by 2 | Viewed by 7787

Abstract

Acceleration is of great importance in motion control for unmanned aerial vehicles (UAVs), especially during the takeoff and landing stages. However, the measured acceleration is inevitably polluted by severe noise. Therefore, a proper noise suppression procedure is required. This paper presents a novel [...] Read more.

Acceleration is of great importance in motion control for unmanned aerial vehicles (UAVs), especially during the takeoff and landing stages. However, the measured acceleration is inevitably polluted by severe noise. Therefore, a proper noise suppression procedure is required. This paper presents a novel method to reduce the noise in the measured vertical acceleration for a thrust-vectored tail-sitter vertical takeoff and landing (VTOL) UAV. In the new procedure, a Kalman filter is first applied to estimate the UAV mass by using the information in the vertical thrust and measured acceleration. The UAV mass is then used to compute an estimate of UAV vertical acceleration. The estimated acceleration is finally fused with the measured acceleration to obtain the minimum variance estimate of vertical acceleration. By doing this, the new approach incorporates the thrust information into the acceleration estimate. The method is applied to the data measured in a VTOL UAV takeoff experiment. Two other denoising approaches developed by former researchers are also tested for comparison. The results demonstrate that the new method is able to suppress the acceleration noise substantially. It also maintains the real-time performance in the final estimated acceleration, which is not seen in the former denoising approaches. The acceleration treated with the new method can be readily used in the motion control applications for UAVs to achieve improved accuracy. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

Development of Cloud-Based UAV Monitoring and Management System

by Mason Itkin, Mihui Kim and Younghee Park

Sensors 2016, 16(11), 1913; https://doi.org/10.3390/s16111913 - 15 Nov 2016

Cited by 46 | Viewed by 8150

Abstract

Unmanned aerial vehicles (UAVs) are an emerging technology with the potential to revolutionize commercial industries and the public domain outside of the military. UAVs would be able to speed up rescue and recovery operations from natural disasters and can be used for autonomous [...] Read more.

Unmanned aerial vehicles (UAVs) are an emerging technology with the potential to revolutionize commercial industries and the public domain outside of the military. UAVs would be able to speed up rescue and recovery operations from natural disasters and can be used for autonomous delivery systems (e.g., Amazon Prime Air). An increase in the number of active UAV systems in dense urban areas is attributed to an influx of UAV hobbyists and commercial multi-UAV systems. As airspace for UAV flight becomes more limited, it is important to monitor and manage many UAV systems using modern collision avoidance techniques. In this paper, we propose a cloud-based web application that provides real-time flight monitoring and management for UAVs. For each connected UAV, detailed UAV sensor readings from the accelerometer, GPS sensor, ultrasonic sensor and visual position cameras are provided along with status reports from the smaller internal components of UAVs (i.e., motor and battery). The dynamic map overlay visualizes active flight paths and current UAV locations, allowing the user to monitor all aircrafts easily. Our system detects and prevents potential collisions by automatically adjusting UAV flight paths and then alerting users to the change. We develop our proposed system and demonstrate its feasibility and performances through simulation. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

Epipolar Rectification with Minimum Perspective Distortion for Oblique Images

by Jianchen Liu, Bingxuan Guo, Wanshou Jiang, Weishu Gong and Xiongwu Xiao

Sensors 2016, 16(11), 1870; https://doi.org/10.3390/s16111870 - 07 Nov 2016

Cited by 4 | Viewed by 6712

Abstract

Epipolar rectification is of great importance for 3D modeling by using UAV (Unmanned Aerial Vehicle) images; however, the existing methods seldom consider the perspective distortion relative to surface planes. Therefore, an algorithm for the rectification of oblique images is proposed and implemented in [...] Read more.

Epipolar rectification is of great importance for 3D modeling by using UAV (Unmanned Aerial Vehicle) images; however, the existing methods seldom consider the perspective distortion relative to surface planes. Therefore, an algorithm for the rectification of oblique images is proposed and implemented in detail. The basic principle is to minimize the rectified images’ perspective distortion relative to the reference planes. First, this minimization problem is formulated as a cost function that is constructed by the tangent value of angle deformation; second, it provides a great deal of flexibility on using different reference planes, such as roofs and the façades of buildings, to generate rectified images. Furthermore, a reasonable scale is acquired according to the dihedral angle between the rectified image plane and the original image plane. The low-quality regions of oblique images are cropped out according to the distortion size. Experimental results revealed that the proposed rectification method can result in improved matching precision (Semi-global dense matching). The matching precision is increased by about 30% for roofs and increased by just 1% for façades, while the façades are not parallel to the baseline. In another designed experiment, the selected façades are parallel to the baseline, the matching precision has a great improvement for façades, by an average of 22%. This fully proves our proposed algorithm that elimination of perspective distortion on rectified images can significantly improve the accuracy of dense matching. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

Towards Autonomous Modular UAV Missions: The Detection, Geo-Location and Landing Paradigm

by Sarantis Kyristsis, Angelos Antonopoulos, Theofilos Chanialakis, Emmanouel Stefanakis, Christos Linardos, Achilles Tripolitsiotis and Panagiotis Partsinevelos

Sensors 2016, 16(11), 1844; https://doi.org/10.3390/s16111844 - 03 Nov 2016

Cited by 41 | Viewed by 9492

Abstract

Nowadays, various unmanned aerial vehicle (UAV) applications become increasingly demanding since they require real-time, autonomous and intelligent functions. Towards this end, in the present study, a fully autonomous UAV scenario is implemented, including the tasks of area scanning, target recognition, geo-location, monitoring, following [...] Read more.

Nowadays, various unmanned aerial vehicle (UAV) applications become increasingly demanding since they require real-time, autonomous and intelligent functions. Towards this end, in the present study, a fully autonomous UAV scenario is implemented, including the tasks of area scanning, target recognition, geo-location, monitoring, following and finally landing on a high speed moving platform. The underlying methodology includes AprilTag target identification through Graphics Processing Unit (GPU) parallelized processing, image processing and several optimized locations and approach algorithms employing gimbal movement, Global Navigation Satellite System (GNSS) readings and UAV navigation. For the experimentation, a commercial and a custom made quad-copter prototype were used, portraying a high and a low-computational embedded platform alternative. Among the successful targeting and follow procedures, it is shown that the landing approach can be successfully performed even under high platform speeds. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

An Analysis of the Influence of Flight Parameters in the Generation of Unmanned Aerial Vehicle (UAV) Orthomosaicks to Survey Archaeological Areas

by Francisco-Javier Mesas-Carrascosa, María Dolores Notario García, Jose Emilio Meroño de Larriva and Alfonso García-Ferrer

Sensors 2016, 16(11), 1838; https://doi.org/10.3390/s16111838 - 01 Nov 2016

Cited by 85 | Viewed by 8652

Abstract

This article describes the configuration and technical specifications of a multi-rotor unmanned aerial vehicle (UAV) using a red–green–blue (RGB) sensor for the acquisition of images needed for the production of orthomosaics to be used in archaeological applications. Several flight missions were programmed as [...] Read more.

This article describes the configuration and technical specifications of a multi-rotor unmanned aerial vehicle (UAV) using a red–green–blue (RGB) sensor for the acquisition of images needed for the production of orthomosaics to be used in archaeological applications. Several flight missions were programmed as follows: flight altitudes at 30, 40, 50, 60, 70 and 80 m above ground level; two forward and side overlap settings (80%–50% and 70%–40%); and the use, or lack thereof, of ground control points. These settings were chosen to analyze their influence on the spatial quality of orthomosaicked images processed by Inpho UASMaster (Trimble, CA, USA). Changes in illumination over the study area, its impact on flight duration, and how it relates to these settings is also considered. The combined effect of these parameters on spatial quality is presented as well, defining a ratio between ground sample distance of UAV images and expected root mean square of a UAV orthomosaick. The results indicate that a balance between all the proposed parameters is useful for optimizing mission planning and image processing, altitude above ground level (AGL) being main parameter because of its influence on root mean square error (RMSE). Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

A Camera-Based Target Detection and Positioning UAV System for Search and Rescue (SAR) Purposes

by Jingxuan Sun, Boyang Li, Yifan Jiang and Chih-yung Wen

Sensors 2016, 16(11), 1778; https://doi.org/10.3390/s16111778 - 25 Oct 2016

Cited by 156 | Viewed by 19150

Abstract

Wilderness search and rescue entails performing a wide-range of work in complex environments and large regions. Given the concerns inherent in large regions due to limited rescue distribution, unmanned aerial vehicle (UAV)-based frameworks are a promising platform for providing aerial imaging. In recent [...] Read more.

Wilderness search and rescue entails performing a wide-range of work in complex environments and large regions. Given the concerns inherent in large regions due to limited rescue distribution, unmanned aerial vehicle (UAV)-based frameworks are a promising platform for providing aerial imaging. In recent years, technological advances in areas such as micro-technology, sensors and navigation have influenced the various applications of UAVs. In this study, an all-in-one camera-based target detection and positioning system is developed and integrated into a fully autonomous fixed-wing UAV. The system presented in this paper is capable of on-board, real-time target identification, post-target identification and location and aerial image collection for further mapping applications. Its performance is examined using several simulated search and rescue missions, and the test results demonstrate its reliability and efficiency. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

Development and Testing of a Two-UAV Communication Relay System

by Boyang Li, Yifan Jiang, Jingxuan Sun, Lingfeng Cai and Chih-Yung Wen

Sensors 2016, 16(10), 1696; https://doi.org/10.3390/s16101696 - 13 Oct 2016

Cited by 54 | Viewed by 14409

Abstract

In the development of beyond-line-of-sight (BLOS) Unmanned Aerial Vehicle (UAV) systems, communication between the UAVs and the ground control station (GCS) is of critical importance. The commonly used economical wireless modules are restricted by the short communication range and are easily blocked by [...] Read more.

In the development of beyond-line-of-sight (BLOS) Unmanned Aerial Vehicle (UAV) systems, communication between the UAVs and the ground control station (GCS) is of critical importance. The commonly used economical wireless modules are restricted by the short communication range and are easily blocked by obstacles. The use of a communication relay system provides a practical way to solve these problems, improving the performance of UAV communication in BLOS and cross-obstacle operations. In this study, a communication relay system, in which a quadrotor was used to relay radio communication for another quadrotor was developed and tested. First, the UAVs used as the airborne platform were constructed, and the hardware for the communication relay system was selected and built up. Second, a set of software programs and protocol for autonomous mission control, communication relay control, and ground control were developed. Finally, the system was fully integrated into the airborne platform and tested both indoor and in-flight. The Received Signal Strength Indication (RSSI) and noise value in two typical application scenarios were recorded. The test results demonstrated the ability of this system to extend the communication range and build communication over obstacles. This system also shows the feasibility to coordinate multiple UAVs’ communication with the same relay structure. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

Spatial Ecology of Estuarine Crocodile (Crocodylus porosus) Nesting in a Fragmented Landscape

by Luke J. Evans, T. Hefin Jones, Keeyen Pang, Silvester Saimin and Benoit Goossens

Sensors 2016, 16(9), 1527; https://doi.org/10.3390/s16091527 - 19 Sep 2016

Cited by 29 | Viewed by 9107

Abstract

The role that oil palm plays in the Lower Kinabatangan region of Eastern Sabah is of considerable scientific and conservation interest, providing a model habitat for many tropical regions as they become increasingly fragmented. Crocodilians, as apex predators, widely distributed throughout the tropics, [...] Read more.

The role that oil palm plays in the Lower Kinabatangan region of Eastern Sabah is of considerable scientific and conservation interest, providing a model habitat for many tropical regions as they become increasingly fragmented. Crocodilians, as apex predators, widely distributed throughout the tropics, are ideal indicator species for ecosystem health. Drones (or unmanned aerial vehicles (UAVs)) were used to identify crocodile nests in a fragmented landscape. Flights were targeted through the use of fuzzy overlay models and nests located primarily in areas indicated as suitable habitat. Nests displayed a number of similarities in terms of habitat characteristics allowing for refined modelling of survey locations. As well as being more cost-effective compared to traditional methods of nesting survey, the use of drones also enabled a larger survey area to be completed albeit with a limited number of flights. The study provides a methodology for targeted nest surveying, as well as a low-cost repeatable flight methodology. This approach has potential for widespread applicability across a range of species and for a variety of study designs. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

Real-Time Robust Tracking for Motion Blur and Fast Motion via Correlation Filters

by Lingyun Xu, Haibo Luo, Bin Hui and Zheng Chang

Sensors 2016, 16(9), 1443; https://doi.org/10.3390/s16091443 - 07 Sep 2016

Cited by 10 | Viewed by 5662

Abstract

Visual tracking has extensive applications in intelligent monitoring and guidance systems. Among state-of-the-art tracking algorithms, Correlation Filter methods perform favorably in robustness, accuracy and speed. However, it also has shortcomings when dealing with pervasive target scale variation, motion blur and fast motion. In [...] Read more.

Visual tracking has extensive applications in intelligent monitoring and guidance systems. Among state-of-the-art tracking algorithms, Correlation Filter methods perform favorably in robustness, accuracy and speed. However, it also has shortcomings when dealing with pervasive target scale variation, motion blur and fast motion. In this paper we proposed a new real-time robust scheme based on Kernelized Correlation Filter (KCF) to significantly improve performance on motion blur and fast motion. By fusing KCF and STC trackers, our algorithm also solve the estimation of scale variation in many scenarios. We theoretically analyze the problem for CFs towards motions and utilize the point sharpness function of the target patch to evaluate the motion state of target. Then we set up an efficient scheme to handle the motion and scale variation without much time consuming. Our algorithm preserves the properties of KCF besides the ability to handle special scenarios. In the end extensive experimental results on benchmark of VOT datasets show our algorithm performs advantageously competed with the top-rank trackers. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

Onboard Robust Visual Tracking for UAVs Using a Reliable Global-Local Object Model

by Changhong Fu, Ran Duan, Dogan Kircali and Erdal Kayacan

Sensors 2016, 16(9), 1406; https://doi.org/10.3390/s16091406 - 31 Aug 2016

Cited by 48 | Viewed by 7228

Abstract

In this paper, we present a novel onboard robust visual algorithm for long-term arbitrary 2D and 3D object tracking using a reliable global-local object model for unmanned aerial vehicle (UAV) applications, e.g., autonomous tracking and chasing a moving target. The first main approach [...] Read more.

In this paper, we present a novel onboard robust visual algorithm for long-term arbitrary 2D and 3D object tracking using a reliable global-local object model for unmanned aerial vehicle (UAV) applications, e.g., autonomous tracking and chasing a moving target. The first main approach in this novel algorithm is the use of a global matching and local tracking approach. In other words, the algorithm initially finds feature correspondences in a way that an improved binary descriptor is developed for global feature matching and an iterative Lucas–Kanade optical flow algorithm is employed for local feature tracking. The second main module is the use of an efficient local geometric filter (LGF), which handles outlier feature correspondences based on a new forward-backward pairwise dissimilarity measure, thereby maintaining pairwise geometric consistency. In the proposed LGF module, a hierarchical agglomerative clustering, i.e., bottom-up aggregation, is applied using an effective single-link method. The third proposed module is a heuristic local outlier factor (to the best of our knowledge, it is utilized for the first time to deal with outlier features in a visual tracking application), which further maximizes the representation of the target object in which we formulate outlier feature detection as a binary classification problem with the output features of the LGF module. Extensive UAV flight experiments show that the proposed visual tracker achieves real-time frame rates of more than thirty-five frames per second on an i7 processor with 640 × 512 image resolution and outperforms the most popular state-of-the-art trackers favorably in terms of robustness, efficiency and accuracy. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images

by Yongzheng Xu, Guizhen Yu, Yunpeng Wang, Xinkai Wu and Yalong Ma

Sensors 2016, 16(8), 1325; https://doi.org/10.3390/s16081325 - 19 Aug 2016

Cited by 107 | Viewed by 11465

Abstract

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive [...] Read more.

A new hybrid vehicle detection scheme which integrates the Viola-Jones (V-J) and linear SVM classifier with HOG feature (HOG + SVM) methods is proposed for vehicle detection from low-altitude unmanned aerial vehicle (UAV) images. As both V-J and HOG + SVM are sensitive to on-road vehicles’ in-plane rotation, the proposed scheme first adopts a roadway orientation adjustment method, which rotates each UAV image to align the roads with the horizontal direction so the original V-J or HOG + SVM method can be directly applied to achieve fast detection and high accuracy. To address the issue of descending detection speed for V-J and HOG + SVM, the proposed scheme further develops an adaptive switching strategy which sophistically integrates V-J and HOG + SVM methods based on their different descending trends of detection speed to improve detection efficiency. A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images. The results also show that the proposed vehicle detection method is competitive compared with other existing vehicle detection methods. Furthermore, since the proposed vehicle detection method can be performed on videos captured from moving UAV platforms without the need of image registration or additional road database, it has great potentials of field applications. Future research will be focusing on expanding the current method for detecting other transportation modes such as buses, trucks, motors, bicycles, and pedestrians. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives

by Tommaso Francesco Villa, Felipe Gonzalez, Branka Miljievic, Zoran D. Ristovski and Lidia Morawska

Sensors 2016, 16(7), 1072; https://doi.org/10.3390/s16071072 - 12 Jul 2016

Cited by 288 | Viewed by 20201

Abstract

Assessment of air quality has been traditionally conducted by ground based monitoring, and more recently by manned aircrafts and satellites. However, performing fast, comprehensive data collection near pollution sources is not always feasible due to the complexity of sites, moving sources or physical [...] Read more.

Assessment of air quality has been traditionally conducted by ground based monitoring, and more recently by manned aircrafts and satellites. However, performing fast, comprehensive data collection near pollution sources is not always feasible due to the complexity of sites, moving sources or physical barriers. Small Unmanned Aerial Vehicles (UAVs) equipped with different sensors have been introduced for in-situ air quality monitoring, as they can offer new approaches and research opportunities in air pollution and emission monitoring, as well as for studying atmospheric trends, such as climate change, while ensuring urban and industrial air safety. The aims of this review were to: (1) compile information on the use of UAVs for air quality studies; and (2) assess their benefits and range of applications. An extensive literature review was conducted using three bibliographic databases (Scopus, Web of Knowledge, Google Scholar) and a total of 60 papers was found. This relatively small number of papers implies that the field is still in its early stages of development. We concluded that, while the potential of UAVs for air quality research has been established, several challenges still need to be addressed, including: the flight endurance, payload capacity, sensor dimensions/accuracy, and sensitivity. However, the challenges are not simply technological, in fact, policy and regulations, which differ between countries, represent the greatest challenge to facilitating the wider use of UAVs in atmospheric research. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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Open AccessArticle

Fast Orientation of Video Images of Buildings Acquired from a UAV without Stabilization

by Michal Kedzierski and Paulina Delis

Sensors 2016, 16(7), 951; https://doi.org/10.3390/s16070951 - 23 Jun 2016

Cited by 13 | Viewed by 5315

Abstract

The aim of this research was to assess the possibility of conducting an absolute orientation procedure for video imagery, in which the external orientation for the first image was typical for aerial photogrammetry whereas the external orientation of the second was typical for [...] Read more.

The aim of this research was to assess the possibility of conducting an absolute orientation procedure for video imagery, in which the external orientation for the first image was typical for aerial photogrammetry whereas the external orientation of the second was typical for terrestrial photogrammetry. Starting from the collinearity equations, assuming that the camera tilt angle is equal to 90°, a simplified mathematical model is proposed. The proposed method can be used to determine the X, Y, Z coordinates of points based on a set of collinearity equations of a pair of images. The use of simplified collinearity equations can considerably shorten the processing tine of image data from Unmanned Aerial Vehicles (UAVs), especially in low cost systems. The conducted experiments have shown that it is possible to carry out a complete photogrammetric project of an architectural structure using a camera tilted 85°–90° (

φ

or ω) and simplified collinearity equations. It is also concluded that there is a correlation between the speed of the UAV and the discrepancy between the established and actual camera tilt angles. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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7602 KiB

Open AccessArticle

An Efficient Seam Elimination Method for UAV Images Based on Wallis Dodging and Gaussian Distance Weight Enhancement

by Jinyan Tian, Xiaojuan Li, Fuzhou Duan, Junqian Wang and Yang Ou

Sensors 2016, 16(5), 662; https://doi.org/10.3390/s16050662 - 10 May 2016

Cited by 21 | Viewed by 5264

Abstract

The rapid development of Unmanned Aerial Vehicle (UAV) remote sensing conforms to the increasing demand for the low-altitude very high resolution (VHR) image data. However, high processing speed of massive UAV data has become an indispensable prerequisite for its applications in various industry [...] Read more.

The rapid development of Unmanned Aerial Vehicle (UAV) remote sensing conforms to the increasing demand for the low-altitude very high resolution (VHR) image data. However, high processing speed of massive UAV data has become an indispensable prerequisite for its applications in various industry sectors. In this paper, we developed an effective and efficient seam elimination approach for UAV images based on Wallis dodging and Gaussian distance weight enhancement (WD-GDWE). The method encompasses two major steps: first, Wallis dodging was introduced to adjust the difference of brightness between the two matched images, and the parameters in the algorithm were derived in this study. Second, a Gaussian distance weight distribution method was proposed to fuse the two matched images in the overlap region based on the theory of the First Law of Geography, which can share the partial dislocation in the seam to the whole overlap region with an effect of smooth transition. This method was validated at a study site located in Hanwang (Sichuan, China) which was a seriously damaged area in the 12 May 2008 enchuan Earthquake. Then, a performance comparison between WD-GDWE and the other five classical seam elimination algorithms in the aspect of efficiency and effectiveness was conducted. Results showed that WD-GDWE is not only efficient, but also has a satisfactory effectiveness. This method is promising in advancing the applications in UAV industry especially in emergency situations. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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544 KiB

Open AccessArticle

Cubature Information SMC-PHD for Multi-Target Tracking

by Zhe Liu, Zulin Wang and Mai Xu

Sensors 2016, 16(5), 653; https://doi.org/10.3390/s16050653 - 09 May 2016

Cited by 8 | Viewed by 4170

Abstract

In multi-target tracking, the key problem lies in estimating the number and states of individual targets, in which the challenge is the time-varying multi-target numbers and states. Recently, several multi-target tracking approaches, based on the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter, [...] Read more.

In multi-target tracking, the key problem lies in estimating the number and states of individual targets, in which the challenge is the time-varying multi-target numbers and states. Recently, several multi-target tracking approaches, based on the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter, have been presented to solve such a problem. However, most of these approaches select the transition density as the importance sampling (IS) function, which is inefficient in a nonlinear scenario. To enhance the performance of the conventional SMC-PHD filter, we propose in this paper two approaches using the cubature information filter (CIF) for multi-target tracking. More specifically, we first apply the posterior intensity as the IS function. Then, we propose to utilize the CIF algorithm with a gating method to calculate the IS function, namely CISMC-PHD approach. Meanwhile, a fast implementation of the CISMC-PHD approach is proposed, which clusters the particles into several groups according to the Gaussian mixture components. With the constructed components, the IS function is approximated instead of particles. As a result, the computational complexity of the CISMC-PHD approach can be significantly reduced. The simulation results demonstrate the effectiveness of our approaches. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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3599 KiB

Open AccessArticle

Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV

by Zain Anwar Ali, Daobo Wang and Muhammad Aamir

Sensors 2016, 16(5), 652; https://doi.org/10.3390/s16050652 - 09 May 2016

Cited by 38 | Viewed by 6848

Abstract

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control [...] Read more.

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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5556 KiB

Open AccessArticle

Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery

by Yalong Ma, Xinkai Wu, Guizhen Yu, Yongzheng Xu and Yunpeng Wang

Sensors 2016, 16(4), 446; https://doi.org/10.3390/s16040446 - 26 Mar 2016

Cited by 83 | Viewed by 13137

Abstract

Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection [...] Read more.

Driven by the prominent thermal signature of humans and following the growing availability of unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on the detection and tracking of pedestrians using thermal infrared images recorded from UAVs. However, pedestrian detection and tracking from the thermal images obtained from UAVs pose many challenges due to the low-resolution of imagery, platform motion, image instability and the relatively small size of the objects. This research tackles these challenges by proposing a pedestrian detection and tracking system. A two-stage blob-based approach is first developed for pedestrian detection. This approach first extracts pedestrian blobs using the regional gradient feature and geometric constraints filtering and then classifies the detected blobs by using a linear Support Vector Machine (SVM) with a hybrid descriptor, which sophisticatedly combines Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT) features in order to achieve accurate detection. This research further proposes an approach for pedestrian tracking. This approach employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data. The proposed detection and tracking approaches have been evaluated by multiple different datasets, and the results illustrate the effectiveness of the proposed methods. This research is expected to significantly benefit many transportation applications, such as the multimodal traffic performance measure, pedestrian behavior study and pedestrian-vehicle crash analysis. Future work will focus on using fused thermal and visual images to further improve the detection efficiency and effectiveness. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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13966 KiB

Open AccessTechnical Note

Low Cost and Flexible UAV Deployment of Sensors

by Lars Yndal Sørensen, Lars Toft Jacobsen and John Paulin Hansen

Sensors 2017, 17(1), 154; https://doi.org/10.3390/s17010154 - 14 Jan 2017

Cited by 45 | Viewed by 10010

Abstract

This paper presents a platform for airborne sensor applications using low-cost, open-source components carried by an easy-to-fly unmanned aircraft vehicle (UAV). The system, available in open-source , is designed for researchers, students and makers for a broad range of exploration and data-collection needs. [...] Read more.

This paper presents a platform for airborne sensor applications using low-cost, open-source components carried by an easy-to-fly unmanned aircraft vehicle (UAV). The system, available in open-source , is designed for researchers, students and makers for a broad range of exploration and data-collection needs. The main contribution is the extensible architecture for modularized airborne sensor deployment and real-time data visualisation. Our open-source Android application provides data collection, flight path definition and map tools. Total cost of the system is below 800 dollars. The flexibility of the system is illustrated by mapping the location of Bluetooth beacons (iBeacons) on a ground field and by measuring water temperature in a lake. Full article

(This article belongs to the Special Issue UAV-Based Remote Sensing)

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