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» Monte Carlo Localization Using SIFT Features
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UAI
2000
14 years 11 months ago
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob...
Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, ...
PRL
2006
139views more  PRL 2006»
14 years 9 months ago
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data
This paper presents a partitional dynamic clustering method for interval data based on adaptive Hausdorff distances. Dynamic clustering algorithms are iterative two-step relocatio...
Francisco de A. T. de Carvalho, Renata M. C. R. de...
ICRA
2010
IEEE
226views Robotics» more  ICRA 2010»
14 years 8 months ago
Indoor scene recognition through object detection
Abstract— Scene recognition is a highly valuable perceptual ability for an indoor mobile robot, however, current approaches for scene recognition present a significant drop in p...
Pablo Espinace, Thomas Kollar, Alvaro Soto, Nichol...
MMAS
2010
Springer
14 years 4 months ago
A Novel Method for Solving Multiscale Elliptic Problems with Randomly Perturbed Data
We propose a method for efficient solution of elliptic problems with multiscale features and randomly perturbed coefficients. We use the multiscale finite element method (MsFEM) as...
Victor Ginting, Axel Målqvist, Michael Presh...
IOLTS
2009
IEEE
124views Hardware» more  IOLTS 2009»
15 years 4 months ago
On-line characterization and reconfiguration for single event upset variations
The amount of physical variation among electronic components on a die is increasing rapidly. There is a need for a better understanding of variations in transient fault susceptibil...
Kenneth M. Zick, John P. Hayes