61,029 research outputs found
Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty
This work proposes a robust visual odometry method for structured
environments that combines point features with line and plane segments,
extracted through an RGB-D camera. Noisy depth maps are processed by a
probabilistic depth fusion framework based on Mixtures of Gaussians to denoise
and derive the depth uncertainty, which is then propagated throughout the
visual odometry pipeline. Probabilistic 3D plane and line fitting solutions are
used to model the uncertainties of the feature parameters and pose is estimated
by combining the three types of primitives based on their uncertainties.
Performance evaluation on RGB-D sequences collected in this work and two public
RGB-D datasets: TUM and ICL-NUIM show the benefit of using the proposed depth
fusion framework and combining the three feature-types, particularly in scenes
with low-textured surfaces, dynamic objects and missing depth measurements.Comment: Major update: more results, depth filter released as opensource, 34
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Model Data Fusion: developing Bayesian inversion to constrain equilibrium and mode structure
Recently, a new probabilistic "data fusion" framework based on Bayesian
principles has been developed on JET and W7-AS. The Bayesian analysis framework
folds in uncertainties and inter-dependencies in the diagnostic data and signal
forward-models, together with prior knowledge of the state of the plasma, to
yield predictions of internal magnetic structure. A feature of the framework,
known as MINERVA (J. Svensson, A. Werner, Plasma Physics and Controlled Fusion
50, 085022, 2008), is the inference of magnetic flux surfaces without the use
of a force balance model. We discuss results from a new project to develop
Bayesian inversion tools that aim to (1) distinguish between competing
equilibrium theories, which capture different physics, using the MAST spherical
tokamak; and (2) test the predictions of MHD theory, particularly mode
structure, using the H-1 Heliac.Comment: submitted to Journal of Plasma Fusion Research 10/11/200
Probabilistic Surfel Fusion for Dense LiDAR Mapping
With the recent development of high-end LiDARs, more and more systems are
able to continuously map the environment while moving and producing spatially
redundant information. However, none of the previous approaches were able to
effectively exploit this redundancy in a dense LiDAR mapping problem. In this
paper, we present a new approach for dense LiDAR mapping using probabilistic
surfel fusion. The proposed system is capable of reconstructing a high-quality
dense surface element (surfel) map from spatially redundant multiple views.
This is achieved by a proposed probabilistic surfel fusion along with a
geometry considered data association. The proposed surfel data association
method considers surface resolution as well as high measurement uncertainty
along its beam direction which enables the mapping system to be able to control
surface resolution without introducing spatial digitization. The proposed
fusion method successfully suppresses the map noise level by considering
measurement noise caused by laser beam incident angle and depth distance in a
Bayesian filtering framework. Experimental results with simulated and real data
for the dense surfel mapping prove the ability of the proposed method to
accurately find the canonical form of the environment without further
post-processing.Comment: Accepted in Multiview Relationships in 3D Data 2017 (IEEE
International Conference on Computer Vision Workshops
Visualization of tokamak operational spaces through the projection of data probability distributions
Information visualization is becoming an increasingly important tool for making inferences from large and complex data sets describing tokamak operational spaces. Landmark MDS, a computationally efficient information visualization tool, well suited to the properties of fusion data, along with a comprehensive probabilistic data representation framework, is shown to provide a structured visual map of plasma confinement regimes, plasma disruption regions and plasma trajectories. This is aimed at contributing to the understanding of underlying physics of various plasma phenomena, while providing an intuitive tool for plasma monitoring
Variational Downscaling, Fusion and Assimilation of Hydrometeorological States via Regularized Estimation
Improved estimation of hydrometeorological states from down-sampled
observations and background model forecasts in a noisy environment, has been a
subject of growing research in the past decades. Here, we introduce a unified
framework that ties together the problems of downscaling, data fusion and data
assimilation as ill-posed inverse problems. This framework seeks solutions
beyond the classic least squares estimation paradigms by imposing proper
regularization, which are constraints consistent with the degree of smoothness
and probabilistic structure of the underlying state. We review relevant
regularization methods in derivative space and extend classic formulations of
the aforementioned problems with particular emphasis on hydrologic and
atmospheric applications. Informed by the statistical characteristics of the
state variable of interest, the central results of the paper suggest that
proper regularization can lead to a more accurate and stable recovery of the
true state and hence more skillful forecasts. In particular, using the Tikhonov
and Huber regularization in the derivative space, the promise of the proposed
framework is demonstrated in static downscaling and fusion of synthetic
multi-sensor precipitation data, while a data assimilation numerical experiment
is presented using the heat equation in a variational setting
A probabilistic framework for believes fusion
This paper presents a methodology to achieve the fusion of believes associated to logical propositions . The belief concept is
introduced within the framework of logic and probability theory and the problem dealt with is the following : if different experts
give their advice about a membership property, how to fuse those believes? This fusion is detailed in three cases : either the experts
speak about the same thing or they speak about different things in a given set, and more generally some experts speaks about
the same thing while the others characterize different objects . The proposed fusion methodology is illustrated by and example
concerning environment building from ultrasonic range scans .Cet article présente un ensemble de méthodes permettant de fusionner des croyances dans des hypothèses représentées par des propositions. La notion de croyance est introduite dans le cadre de la logique des propositions et du calcul des probabilités. Le problème à traiter est le suivant : si différents experts fournissent à un système plusieurs croyances à propos d'une hypothèse ensembliste, comment ce système peut-il fusionner ces croyances? Cette fusion est envisagée dans trois cas de figure : soit les experts caractérisent le même élément, soit ils caractérisent des éléments différents appartenant à un même ensemble, soit certains caractérisent le même élément et d'autres des éléments différents. La méthodologie développée est illustrée sur un exemple de fusion multi-capteurs pour des télémètres à ultrasons utilisés en robotique mobile
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