111 research outputs found
Fusion of Real Time Thermal Image and 1D/2D/3D Depth Laser Readings for Remote Thermal Sensing in Industrial Plants by Means of UAVs and/or Robots
This paper presents fast procedures for thermal infrared remote sensing in
dark, GPS-denied environments, such as those found in industrial plants such as
in High-Voltage Direct Current (HVDC) converter stations. These procedures are
based on the combination of the depth estimation obtained from either a
1-Dimensional LIDAR laser or a 2-Dimensional Hokuyo laser or a 3D MultiSense
SLB laser sensor and the visible and thermal cameras from a FLIR Duo R
dual-sensor thermal camera. The combination of these sensors/cameras is
suitable to be mounted on Unmanned Aerial Vehicles (UAVs) and/or robots in
order to provide reliable information about the potential malfunctions, which
can be found within the hazardous environment. For example, the capabilities of
the developed software and hardware system corresponding to the combination of
the 1-D LIDAR sensor and the FLIR Duo R dual-sensor thermal camera is assessed
from the point of the accuracy of results and the required computational times:
the obtained computational times are under 10 ms, with a maximum localization
error of 8 mm and an average standard deviation for the measured temperatures
of 1.11 degree Celsius, which results are obtained for a number of test cases.
The paper is structured as follows: the description of the system used for
identification and localization of hotspots in industrial plants is presented
in section II. In section III, the method for faults identification and
localization in plants by using a 1-Dimensional LIDAR laser sensor and thermal
images is described together with results. In section IV the real time thermal
image processing is presented. Fusion of the 2-Dimensional depth laser Hokuyo
and the thermal images is described in section V. In section VI the combination
of the 3D MultiSense SLB laser and thermal images is described. In section VII
a discussion and several conclusions are drawn
HAND GESTURES FOR DRONE CONTROL USING DEEP LEARNING
Commercial drones, also known as unmanned aerial vehicles (UAVs), are rapidly becoming more prevalent and are used in many different applications, such as surveillance for sporting events, transportation for emergency equipment and goods, filming and aerial photography and many other activities. The number of drones in United States of America are forecast to double and grow from an estimated 1.1 million units in 2017 to reach 2.4 million units by 2022 according to the Federal Aviation Administration (FAA) [1]. The fact that most of the drones can carry payloads has encouraged many of the drone manufacturing companies to add different types of sensors to drones, and the most basic one is the camera. The previous reasons have led to open a new field of study which is called Drone-Human Interface (HDI) and user interface researchers have started studying the different possible ways to interact with drones ranging from the traditional devices like Radio Controller (RC) to controlling the drones using human body postures.
This thesis presents research detailing the use of hand gestures as a HDI method to control the drones. This work consists of three main modules: Hand Detector, Gesture Recognizer and Drone Controller. A deep learning method is incorporated and utilized in the first module to detect and track the hands in real-time with high accuracy and from a single Red-Blue-Green (RGB) image. Image processing algorithms and techniques are introduced as a dynamic way to identify the hand gestures and motions. Finally, the Drone Controller module is responsible for communicating with the drones. It sends and receives the messages between the proposed system and the drone which is connected to the system
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Nonparametric background modelling and segmentation to detect Micro Air Vehicles (MAV) using RGB-D Sensor
A novel approach to detect Micro Air Vehicles in GPS-denied environments using an external RGB-D sensor is presented. The nonparametric background subtraction technique incorporating several innovative mechanisms allows the detection of high-speed moving MAVs by combining colour and depth information. The proposed method stores several colour and depth images as a model and then compares each pixel from a frame with the stored models to classify the pixel as background or foreground. To adapt to scene changes, once a pixel is classified as background, the system updates the model by finding and substituting the closest pixel to the camera with the current pixel. The background-model update presented uses different criteria from existing methods. Additionally, a blind-update model is added to adapt to background sudden changes. The proposed architecture is compared with existing techniques using two different MAVs and publicly available datasets. Results showing some improvements over existing methods are discussed
The Complete Reference (Volume 4)
This is the fourth volume of the successful series Robot Operating Systems: The Complete Reference, providing a comprehensive overview of robot operating systems (ROS), which is currently the main development framework for robotics applications, as well as the latest trends and contributed systems. The book is divided into four parts: Part 1 features two papers on navigation, discussing SLAM and path planning. Part 2 focuses on the integration of ROS into quadcopters and their control. Part 3 then discusses two emerging applications for robotics: cloud robotics, and video stabilization. Part 4 presents tools developed for ROS; the first is a practical alternative to the roslaunch system, and the second is related to penetration testing. This book is a valuable resource for ROS users and wanting to learn more about ROS capabilities and features.info:eu-repo/semantics/publishedVersio
Obstacle avoidance based-visual navigation for micro aerial vehicles
This paper describes an obstacle avoidance system for low-cost Unmanned Aerial Vehicles (UAVs) using vision as the principal source of information through the monocular onboard camera. For detecting obstacles, the proposed system compares the image obtained in real time from the UAV with a database of obstacles that must be avoided. In our proposal, we include the feature point detector Speeded Up Robust Features (SURF) for fast obstacle detection and a control law to avoid them. Furthermore, our research includes a path recovery algorithm. Our method is attractive for compact MAVs in which other sensors will not be implemented. The system was tested in real time on a Micro Aerial Vehicle (MAV), to detect and avoid obstacles in an unknown controlled environment; we compared our approach with related works.Peer ReviewedPostprint (published version
Biologically Inspired Visual Control of Flying Robots
Insects posses an incredible ability to navigate their environment at high speed, despite
having small brains and limited visual acuity. Through selective pressure they have
evolved computationally efficient means for simultaneously performing navigation tasks
and instantaneous control responses. The insect’s main source of information is visual,
and through a hierarchy of processes this information is used for perception; at the
lowest level are local neurons for detecting image motion and edges, at the higher level
are interneurons to spatially integrate the output of previous stages. These higher
level processes could be considered as models of the insect's environment, reducing the
amount of information to only that which evolution has determined relevant. The scope
of this thesis is experimenting with biologically inspired visual control of flying robots
through information processing, models of the environment, and flight behaviour.
In order to test these ideas I developed a custom quadrotor robot and experimental
platform; the 'wasp' system. All algorithms ran on the robot, in real-time or better,
and hypotheses were always verified with flight experiments.
I developed a new optical flow algorithm that is computationally efficient, and able
to be applied in a regular pattern to the image. This technique is used later in my
work when considering patterns in the image motion field.
Using optical flow in the log-polar coordinate system I developed attitude estimation
and time-to-contact algorithms. I find that the log-polar domain is useful for
analysing global image motion; and in many ways equivalent to the retinotopic arrange-
ment of neurons in the optic lobe of insects, used for the same task.
I investigated the role of depth in insect flight using two experiments. In the first
experiment, to study how concurrent visual control processes might be combined, I
developed a control system using the combined output of two algorithms. The first
algorithm was a wide-field optical flow balance strategy and the second an obstacle
avoidance strategy which used inertial information to estimate the depth to objects in
the environment - objects whose depth was significantly different to their surround-
ings. In the second experiment I created an altitude control system which used a model
of the environment in the Hough space, and a biologically inspired sampling strategy,
to efficiently detect the ground. Both control systems were used to control the flight
of a quadrotor in an indoor environment.
The methods that insects use to perceive edges and control their flight in response
had not been applied to artificial systems before. I developed a quadrotor control
system that used the distribution of edges in the environment to regulate the robot
height and avoid obstacles. I also developed a model that predicted the distribution of
edges in a static scene, and using this prediction was able to estimate the quadrotor
altitude
Virtuaalse proovikabiini 3D kehakujude ja roboti juhtimisalgoritmide uurimine
Väitekirja elektrooniline versioon ei sisalda publikatsiooneVirtuaalne riiete proovimine on üks põhilistest teenustest, mille pakkumine võib suurendada rõivapoodide edukust, sest tänu sellele lahendusele väheneb füüsilise töö vajadus proovimise faasis ning riiete proovimine muutub kasutaja jaoks mugavamaks. Samas pole enamikel varem välja pakutud masinnägemise ja graafika meetoditel õnnestunud inimkeha realistlik modelleerimine, eriti terve keha 3D modelleerimine, mis vajab suurt kogust andmeid ja palju arvutuslikku ressurssi. Varasemad katsed on ebaõnnestunud põhiliselt seetõttu, et ei ole suudetud korralikult arvesse võtta samaaegseid muutusi keha pinnal. Lisaks pole varasemad meetodid enamasti suutnud kujutiste liikumisi realistlikult reaalajas visualiseerida. Käesolev projekt kavatseb kõrvaldada eelmainitud puudused nii, et rahuldada virtuaalse proovikabiini vajadusi. Välja pakutud meetod seisneb nii kasutaja keha kui ka riiete skaneerimises, analüüsimises, modelleerimises, mõõtmete arvutamises, orientiiride paigutamises, mannekeenidelt võetud 3D visuaalsete andmete segmenteerimises ning riiete mudeli paigutamises ja visualiseerimises kasutaja kehal. Selle projekti käigus koguti visuaalseid andmeid kasutades 3D laserskannerit ja Kinecti optilist kaamerat ning koostati nendest andmebaas. Neid andmeid kasutati välja töötatud algoritmide testimiseks, mis peamiselt tegelevad riiete realistliku visuaalse kujutamisega inimkehal ja suuruse pakkumise süsteemi täiendamisega virtuaalse proovikabiini kontekstis.Virtual fitting constitutes a fundamental element of the developments expected to rise the commercial prosperity of online garment retailers to a new level, as it is expected to reduce the load of the manual labor and physical efforts required. Nevertheless, most of the previously proposed computer vision and graphics methods have failed to accurately and realistically model the human body, especially, when it comes to the 3D modeling of the whole human body. The failure is largely related to the huge data and calculations required, which in reality is caused mainly by inability to properly account for the simultaneous variations in the body surface. In addition, most of the foregoing techniques cannot render realistic movement representations in real-time. This project intends to overcome the aforementioned shortcomings so as to satisfy the requirements of a virtual fitting room. The proposed methodology consists in scanning and performing some specific analyses of both the user's body and the prospective garment to be virtually fitted, modeling, extracting measurements and assigning reference points on them, and segmenting the 3D visual data imported from the mannequins. Finally, superimposing, adopting and depicting the resulting garment model on the user's body. The project is intended to gather sufficient amounts of visual data using a 3D laser scanner and the Kinect optical camera, to manage it in form of a usable database, in order to experimentally implement the algorithms devised. The latter will provide a realistic visual representation of the garment on the body, and enhance the size-advisor system in the context of the virtual fitting room under study
AN ASSESSMENT OF 2D AND 3D SPATIAL ACCURACY OF PHOTOGRAMMETRY FOR LIVESTOCK HEALTH MONITORING
The overall objective of this study was to evaluate the use of consumer-grade unmanned aircraft systems for image-based remote sensing in agriculture with application towards livestock health monitoring. A two-dimensional spatial error experiment was conducted to quantify the spatial accuracy of georeferenced orthomosaic imagery collected using a drone and processed with photogrammetry software. Treatment variables included altitude above ground level and image data type (visible and multispectral). The results from the ANOVA test indicated that there were significant differences between data types, but no significant differences between altitudes. The experiment was then expanded to a three-dimensional study where two life-size cow statues were extensively photographed and processed in groupings to simulate different flights formations. The resulting volume estimations from groups of images were compared to the estimate when using all images. Results revealed the importance of selecting the right flight paths to produce the most c 3D model
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