6,711 research outputs found
Visual-inertial self-calibration on informative motion segments
Environmental conditions and external effects, such as shocks, have a
significant impact on the calibration parameters of visual-inertial sensor
systems. Thus long-term operation of these systems cannot fully rely on factory
calibration. Since the observability of certain parameters is highly dependent
on the motion of the device, using short data segments at device initialization
may yield poor results. When such systems are additionally subject to energy
constraints, it is also infeasible to use full-batch approaches on a big
dataset and careful selection of the data is of high importance. In this paper,
we present a novel approach for resource efficient self-calibration of
visual-inertial sensor systems. This is achieved by casting the calibration as
a segment-based optimization problem that can be run on a small subset of
informative segments. Consequently, the computational burden is limited as only
a predefined number of segments is used. We also propose an efficient
information-theoretic selection to identify such informative motion segments.
In evaluations on a challenging dataset, we show our approach to significantly
outperform state-of-the-art in terms of computational burden while maintaining
a comparable accuracy
A new framework for extracting coarse-grained models from time series with multiscale structure
In many applications it is desirable to infer coarse-grained models from
observational data. The observed process often corresponds only to a few
selected degrees of freedom of a high-dimensional dynamical system with
multiple time scales. In this work we consider the inference problem of
identifying an appropriate coarse-grained model from a single time series of a
multiscale system. It is known that estimators such as the maximum likelihood
estimator or the quadratic variation of the path estimator can be strongly
biased in this setting. Here we present a novel parametric inference
methodology for problems with linear parameter dependency that does not suffer
from this drawback. Furthermore, we demonstrate through a wide spectrum of
examples that our methodology can be used to derive appropriate coarse-grained
models from time series of partial observations of a multiscale system in an
effective and systematic fashion
An alternative solution to the model structure selection problem
An alternative solution to the model structure selection problem is introduced by conducting a forward search through the many possible candidate model terms initially and then performing an exhaustive all subset model selection on the resulting model. An example is included to demonstrate that this approach leads to dynamically valid nonlinear model
Local Parametric Estimation in High Frequency Data
In this paper, we give a general time-varying parameter model, where the
multidimensional parameter possibly includes jumps. The quantity of interest is
defined as the integrated value over time of the parameter process . We provide a local parametric estimator (LPE)
of and conditions under which we can show the central limit theorem.
Roughly speaking those conditions correspond to some uniform limit theory in
the parametric version of the problem. The framework is restricted to the
specific convergence rate . Several examples of LPE are studied:
estimation of volatility, powers of volatility, volatility when incorporating
trading information and time-varying MA(1).Comment: 67 pages, 4 figure
Non-parametric PSF estimation from celestial transit solar images using blind deconvolution
Context: Characterization of instrumental effects in astronomical imaging is
important in order to extract accurate physical information from the
observations. The measured image in a real optical instrument is usually
represented by the convolution of an ideal image with a Point Spread Function
(PSF). Additionally, the image acquisition process is also contaminated by
other sources of noise (read-out, photon-counting). The problem of estimating
both the PSF and a denoised image is called blind deconvolution and is
ill-posed.
Aims: We propose a blind deconvolution scheme that relies on image
regularization. Contrarily to most methods presented in the literature, our
method does not assume a parametric model of the PSF and can thus be applied to
any telescope.
Methods: Our scheme uses a wavelet analysis prior model on the image and weak
assumptions on the PSF. We use observations from a celestial transit, where the
occulting body can be assumed to be a black disk. These constraints allow us to
retain meaningful solutions for the filter and the image, eliminating trivial,
translated and interchanged solutions. Under an additive Gaussian noise
assumption, they also enforce noise canceling and avoid reconstruction
artifacts by promoting the whiteness of the residual between the blurred
observations and the cleaned data.
Results: Our method is applied to synthetic and experimental data. The PSF is
estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for
SDO/AIA using the 2012 Venus transit. Results show that the proposed
non-parametric blind deconvolution method is able to estimate the core of the
PSF with a similar quality to parametric methods proposed in the literature. We
also show that, if these parametric estimations are incorporated in the
acquisition model, the resulting PSF outperforms both the parametric and
non-parametric methods.Comment: 31 pages, 47 figure
- …