18 research outputs found
Application of metaheuristic optimization algorithms for image registration in mobile robot visual control
Visual Servoing (VS) of a mobile robot requires advanced digital image processing, and one of the techniques especially fitting for this complex task is Image Registration (IR). In general, IR involves the geometrical alignment of images, and it can be viewed as an optimization problem. Therefore, we propose Metaheuristic Optimization Algorithms (MOA) for IR in VS of a mobile robot. The comprehensive comparison study of three state-of-the-art MOA, namely the Slime Mould Algorithm (SMA), Harris Hawks Optimizer (HHO), and Whale Optimization Algorithm (WOA) is presented. The previously mentioned MOA used for IR are evaluated on 12 pairs of stereo images obtained by a mobile robot stereo vision system in a laboratory model of a manufacturing environment. The MATLAB software package is used for the implementation of the considered optimization algorithms. Acquired experimental results show that SMA outperforms HHO and WOA, while all three algorithms perform satisfactory alignment of images captured from various mobile robot poses
Application of metaheuristic optimization algorithms for image registration in mobile robot visual control
Visual Servoing (VS) of a mobile robot requires advanced digital image processing, and one of the techniques especially fitting for this complex task is Image Registration (IR). In general, IR involves the geometrical alignment of images, and it can be viewed as an optimization problem. Therefore, we propose Metaheuristic Optimization Algorithms (MOA) for IR in VS of a mobile robot. The comprehensive comparison study of three state-of-the-art MOA, namely the Slime Mould Algorithm (SMA), Harris Hawks Optimizer (HHO), and Whale Optimization Algorithm (WOA) is presented. The previously mentioned MOA used for IR are evaluated on 12 pairs of stereo images obtained by a mobile robot stereo vision system in a laboratory model of a manufacturing environment. The MATLAB software package is used for the implementation of the considered optimization algorithms. Acquired experimental results show that SMA outperforms HHO and WOA, while all three algorithms perform satisfactory alignment of images captured from various mobile robot poses
Modelling the Xbox 360 Kinect for visual servo control applications
A research report submitted to the faculty of Engineering and the built environment, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering.
Johannesburg, August 2016There has been much interest in using the Microsoft Xbox 360 Kinect
cameras for visual servo control applications. It is a relatively cheap
device with expected shortcomings. This work contributes to the practical
considerations of using the Kinect for visual servo control applications.
A comprehensive characterisation of the Kinect is synthesised
from existing literature and results from a nonlinear calibration procedure.
The Kinect reduces computational overhead on image processing
stages, such as pose estimation or depth estimation. It is limited
by its 0.8m to 3.5m practical depth range and quadratic depth resolution
of 1.8mm to 35mm, respectively. Since the Kinect uses an
infra-red (IR) projector, a class one laser, it should not be used outdoors,
due to IR saturation, and objects belonging to classes of non-
IR-friendly surfaces should be avoided, due to IR refraction, absorption,
or specular reflection. Problems of task stability due to invalid
depth measurements in Kinect depth maps and practical depth range
limitations can be reduced by using depth map preprocessing and
activating classical visual servoing techniques when Kinect-based approaches
are near task failure.MT201
Photometric moments: New promising candidates for visual servoing
International audienceIn this paper, we propose a new type of visual features for visual servoing : photometric moments. These global features do not require any segmentation, matching or tracking steps. The analytical form of the interaction matrix is developed in closed form for these features. Results from experiments carried out with photometric moments have been presented. The results validate our modelling and the control scheme. They perform well for large camera displacements and are endowed with a large convergence domain. From the properties exhibited, photometric moments hold promise as better candidates for IBVS over currently existing geometric and pure luminance features
Biologically Inspired Optimization Methods for Image Registration in Visual Servoing of a Mobile Robot
Image registration (IR) represents image processing
technique that is suitable for use in Visual Servoing (VS). This
paper proposes the use of Biologically Inspired Optimization
(BIO) methods for IR in VS of nonholonomic mobile robot. The
comparison study of three different BIO methods is conducted,
namely Genetic Algorithm (GA), Particle Swarm Optimization
(PSO), and Grey Wolf Optimizer (GWO). The aforementioned
optimization algorithms utilized for IR are tested on 24 images of
manufacturing entities acquired by mobile robot stereo vision
system. The considered algorithms are implemented in the
MATLAB environment. The experimental results suggest
satisfactory geometrical alignment after IR, whilst GA and PSO
outperform GWO
Object distance measurement using a single camera for robotic applications
Visual servoing is defined as controlling robots by extracting data obtained from
the vision system, such as the distance of an object with respect to a reference frame, or the length and width of the object. There are three image-based object distance
measurement techniques: i) using two cameras, i.e., stereovision; ii) using a single
camera, i.e., monovision; and iii) time-of-flight camera.
The stereovision method uses two cameras to find the object’s depth and is highly
accurate. However, it is costly compared to the monovision technique due to the higher
computational burden and the cost of two cameras (rather than one) and related
accessories. In addition, in stereovision, a larger number of images of the object need to
be processed in real-time, and by increasing the distance of the object from cameras, the
measurement accuracy decreases. In the time-of-flight distance measurement technique,
distance information is obtained by measuring the total time for the light to transmit to
and reflect from the object. The shortcoming of this technique is that it is difficult to
separate the incoming signal, since it depends on many parameters such as the intensity
of the reflected light, the intensity of the background light, and the dynamic range of the
sensor. However, for applications such as rescue robot or object manipulation by a robot
in a home and office environment, the high accuracy distance measurement provided by
stereovision is not required. Instead, the monovision approach is attractive for some
applications due to: i) lower cost and lower computational burden; and ii) lower
complexity due to the use of only one camera.
Using a single camera for distance measurement, object detection and feature
extraction (i.e., finding the length and width of an object) is not yet well researched and there are very few published works on the topic in the literature. Therefore, using this
technique for real-world robotics applications requires more research and improvements.
This thesis mainly focuses on the development of object distance measurement
and feature extraction algorithms using a single fixed camera and a single camera with
variable pitch angle based on image processing techniques. As a result, two different
improved and modified object distance measurement algorithms were proposed for cases
where a camera is fixed at a given angle in the vertical plane and when it is rotating in a
vertical plane. In the proposed algorithms, as a first step, the object distance and
dimension such as length and width were obtained using existing image processing
techniques. Since the results were not accurate due to lens distortion, noise, variable light
intensity and other uncertainties such as deviation of the position of the object from the
optical axes of camera, in the second step, the distance and dimension of the object
obtained from existing techniques were modified in the X- and Y-directions and for the
orientation of the object about the Z-axis in the object plane by using experimental data
and identification techniques such as the least square method.
Extensive experimental results confirmed that the accuracy increased for
measured distance from 9.4 mm to 2.95 mm, for length from 11.6 mm to 2.2 mm, and for
width from 18.6 mm to 10.8 mm. In addition, the proposed algorithm is significantly
improved with proposed corrections compared to existing methods. Furthermore, the
improved distance measurement method is computationally efficient and can be used for
real-time robotic application tasks such as pick and place and object manipulation in a
home or office environment.Master's Thesi
Collision avoidance on unmanned aerial vehicles using neural network pipelines and flow clustering techniques
UIDB/04111/2020
PCIF/SSI/0102/2017
IF/00325/2015Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.publishersversionpublishe
Image Analysis via Applied Harmonic Analysis : Perceptual Image Quality Assessment, Visual Servoing, and Feature Detection
Certain systems of analyzing functions developed in the field of applied harmonic analysis are specifically designed to yield efficient representations of structures which are characteristic of common classes of two-dimensional signals, like images. In particular, functions in these systems are typically sensitive to features that define the geometry of a signal, like edges and curves in the case of images. These properties make them ideal candidates for a wide variety of tasks in image processing and image analysis. This thesis discusses three recently developed approaches to utilizing systems of wavelets, shearlets, and alpha-molecules in specific image analysis tasks. First, a perceptual image similarity measure is introduced that is solely based on the coefficients obtained from six discrete Haar wavelet filters but yields state of the art correlations with human opinion scores on large benchmark databases. The second application concerns visual servoing, which is a technique for controlling the motion of a robot by using feedback from a visual sensor. In particular, it will be investigated how the coefficients yielded by discrete wavelet and shearlet transforms can be used as the visual features that control the motion of a robot with six degrees of freedom. Finally, a novel framework for the detection and characterization of features such as edges, ridges, and blobs in two-dimensional images is presented and evaluated in extensive numerical experiments. Here, versatile and robust feature detectors are obtained by exploiting the special symmetry properties of directionally sensitive analyzing functions in systems created within the recently introduced alpha-molecule framework
Image Analysis via Applied Harmonic Analysis : Perceptual Image Quality Assessment, Visual Servoing, and Feature Detection
Certain systems of analyzing functions developed in the field of applied harmonic analysis are specifically designed to yield efficient representations of structures which are characteristic of common classes of two-dimensional signals, like images. In particular, functions in these systems are typically sensitive to features that define the geometry of a signal, like edges and curves in the case of images. These properties make them ideal candidates for a wide variety of tasks in image processing and image analysis. This thesis discusses three recently developed approaches to utilizing systems of wavelets, shearlets, and alpha-molecules in specific image analysis tasks. First, a perceptual image similarity measure is introduced that is solely based on the coefficients obtained from six discrete Haar wavelet filters but yields state of the art correlations with human opinion scores on large benchmark databases. The second application concerns visual servoing, which is a technique for controlling the motion of a robot by using feedback from a visual sensor. In particular, it will be investigated how the coefficients yielded by discrete wavelet and shearlet transforms can be used as the visual features that control the motion of a robot with six degrees of freedom. Finally, a novel framework for the detection and characterization of features such as edges, ridges, and blobs in two-dimensional images is presented and evaluated in extensive numerical experiments. Here, versatile and robust feature detectors are obtained by exploiting the special symmetry properties of directionally sensitive analyzing functions in systems created within the recently introduced alpha-molecule framework