4,742 research outputs found
A General Bayesian Framework for Ellipse-based and Hyperbola-based Damage Localisation in Anisotropic Composite Plates
This paper focuses on Bayesian Lamb wave-based damage localization in structural health monitoring of anisotropic composite materials. A Bayesian framework is applied to take account for uncertainties from experimental time-of-flight measurements and angular dependent group velocity within the composite material. An original parametric analytical expression of the direction dependence of group velocity is proposed and validated numerically and experimentally for anisotropic composite and sandwich plates. This expression is incorporated into time-of-arrival (ToA: ellipse-based) and time-difference-of-arrival (TDoA: hyperbola-based) Bayesian damage localization algorithms. This way, the damage location as well as the group velocity profile are estimated jointly and a priori information taken into consideration. The proposed algorithm is general as it allows to take into account for uncertainties within a Bayesian framework, and to model effects of anisotropy on group velocity. Numerical and experimental results obtained with different damage sizes or locations and for different degrees of anisotropy validate the ability of the proposed algorithm to estimate both the damage location and the group velocity profile as well as the associated confidence intervals. Results highlight the need to consider for anisotropy in order to increase localization accuracy, and to use Bayesian analysis to quantify uncertainties in damage localization.Projet CORALI
A practical multirobot localization system
We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with a millimeter precision. In addition, we present the method's mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at \emph{http://purl.org/robotics/whycon}; so, it can be used as an enabling technology for various mobile robotic problems
Geometric Interpretation of Theoretical Bounds for RSS-based Source Localization with Uncertain Anchor Positions
The Received Signal Strength based source localization can encounter severe
problems originating from uncertain information about the anchor positions in
practice. The anchor positions, although commonly assumed to be precisely known
prior to the source localization, are usually obtained using previous
estimation algorithm such as GPS. This previous estimation procedure produces
anchor positions with limited accuracy that result in degradations of the
source localization algorithm and topology uncertainty. We have recently
addressed the problem with a joint estimation framework that jointly estimates
the unknown source and uncertain anchors positions and derived the theoretical
limits of the framework. This paper extends the authors previous work on the
theoretical performance bounds of the joint localization framework with
appropriate geometric interpretation of the overall problem exploiting the
properties of semi-definiteness and symmetry of the Fisher Information Matrix
and the Cram{\`e}r-Rao Lower Bound and using Information and Error Ellipses,
respectively. The numerical results aim to illustrate and discuss the
usefulness of the geometric interpretation. They provide in-depth insight into
the geometrical properties of the joint localization problem underlining the
various possibilities for practical design of efficient localization
algorithms.Comment: 30 pages, 15 figure
Comparison of Gravitational Wave Detector Network Sky Localization Approximations
Gravitational waves emitted during compact binary coalescences are a
promising source for gravitational-wave detector networks. The accuracy with
which the location of the source on the sky can be inferred from gravitational
wave data is a limiting factor for several potential scientific goals of
gravitational-wave astronomy, including multi-messenger observations. Various
methods have been used to estimate the ability of a proposed network to
localize sources. Here we compare two techniques for predicting the uncertainty
of sky localization -- timing triangulation and the Fisher information matrix
approximations -- with Bayesian inference on the full, coherent data set. We
find that timing triangulation alone tends to over-estimate the uncertainty in
sky localization by a median factor of for a set of signals from
non-spinning compact object binaries ranging up to a total mass of , and the over-estimation increases with the mass of the system. We
find that average predictions can be brought to better agreement by the
inclusion of phase consistency information in timing-triangulation techniques.
However, even after corrections, these techniques can yield significantly
different results to the full analysis on specific mock signals. Thus, while
the approximate techniques may be useful in providing rapid, large scale
estimates of network localization capability, the fully coherent Bayesian
analysis gives more robust results for individual signals, particularly in the
presence of detector noise.Comment: 11 pages, 7 Figure
Implicit Cooperative Positioning in Vehicular Networks
Absolute positioning of vehicles is based on Global Navigation Satellite
Systems (GNSS) combined with on-board sensors and high-resolution maps. In
Cooperative Intelligent Transportation Systems (C-ITS), the positioning
performance can be augmented by means of vehicular networks that enable
vehicles to share location-related information. This paper presents an Implicit
Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle
(V2V) connectivity in an innovative manner, avoiding the use of explicit V2V
measurements such as ranging. In the ICP approach, vehicles jointly localize
non-cooperative physical features (such as people, traffic lights or inactive
cars) in the surrounding areas, and use them as common noisy reference points
to refine their location estimates. Information on sensed features are fused
through V2V links by a consensus procedure, nested within a message passing
algorithm, to enhance the vehicle localization accuracy. As positioning does
not rely on explicit ranging information between vehicles, the proposed ICP
method is amenable to implementation with off-the-shelf vehicular communication
hardware. The localization algorithm is validated in different traffic
scenarios, including a crossroad area with heterogeneous conditions in terms of
feature density and V2V connectivity, as well as a real urban area by using
Simulation of Urban MObility (SUMO) for traffic data generation. Performance
results show that the proposed ICP method can significantly improve the vehicle
location accuracy compared to the stand-alone GNSS, especially in harsh
environments, such as in urban canyons, where the GNSS signal is highly
degraded or denied.Comment: 15 pages, 10 figures, in review, 201
Localizing coalescing massive black hole binaries with gravitational waves
Massive black hole binary coalescences are prime targets for space-based
gravitational wave (GW) observatories such as {\it LISA}. GW measurements can
localize the position of a coalescing binary on the sky to an ellipse with a
major axis of a few tens of arcminutes to a few degrees, depending on source
redshift, and a minor axis which is times smaller. Neglecting weak
gravitational lensing, the GWs would also determine the source's luminosity
distance to better than percent accuracy for close sources, degrading to
several percent for more distant sources. Weak lensing cannot, in fact, be
neglected and is expected to limit the accuracy with which distances can be
fixed to errors no less than a few percent. Assuming a well-measured cosmology,
the source's redshift could be inferred with similar accuracy. GWs alone can
thus pinpoint a binary to a three-dimensional ``pixel'' which can help guide
searches for the hosts of these events. We examine the time evolution of this
pixel, studying it at merger and at several intervals before merger. One day
before merger, the major axis of the error ellipse is typically larger than its
final value by a factor of . The minor axis is larger by a factor
of , and, neglecting lensing, the error in the luminosity distance is
larger by a factor of . This large change over a short period of
time is due to spin-induced precession, which is strongest in the final days
before merger. The evolution is slower as we go back further in time. For , we find that GWs will localize a coalescing binary to within $\sim 10\
\mathrm{deg}^2$ as early as a month prior to merger and determine distance (and
hence redshift) to several percent.Comment: 30 pages, 10 figures, 5 tables. Version published in Ap
Calibration and Sensitivity Analysis of a Stereo Vision-Based Driver Assistance System
Az http://intechweb.org/ alatti "Books" fül alatt kell rákeresni a "Stereo Vision" címre és az 1. fejezetre
RT-SLAM: A Generic and Real-Time Visual SLAM Implementation
This article presents a new open-source C++ implementation to solve the SLAM
problem, which is focused on genericity, versatility and high execution speed.
It is based on an original object oriented architecture, that allows the
combination of numerous sensors and landmark types, and the integration of
various approaches proposed in the literature. The system capacities are
illustrated by the presentation of an inertial/vision SLAM approach, for which
several improvements over existing methods have been introduced, and that copes
with very high dynamic motions. Results with a hand-held camera are presented.Comment: 10 page
Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images
Iris centre localization in low-resolution visible images is a challenging
problem in computer vision community due to noise, shadows, occlusions, pose
variations, eye blinks, etc. This paper proposes an efficient method for
determining iris centre in low-resolution images in the visible spectrum. Even
low-cost consumer-grade webcams can be used for gaze tracking without any
additional hardware. A two-stage algorithm is proposed for iris centre
localization. The proposed method uses geometrical characteristics of the eye.
In the first stage, a fast convolution based approach is used for obtaining the
coarse location of iris centre (IC). The IC location is further refined in the
second stage using boundary tracing and ellipse fitting. The algorithm has been
evaluated in public databases like BioID, Gi4E and is found to outperform the
state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201
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