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» Representing and generating uncertainty effectively
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ICRA
1999
IEEE
122views Robotics» more  ICRA 1999»
15 years 3 months ago
Learning Visual Landmarks for Pose Estimation
Abstract-- We present an approach to vision-based mobile robot localization, even without an a-priori pose estimate. This is accomplished by learning a set of visual features calle...
Robert Sim, Gregory Dudek
AUSAI
2008
Springer
15 years 1 months ago
Using Gaussian Processes to Optimize Expensive Functions
The task of finding the optimum of some function f(x) is commonly accomplished by generating and testing sample solutions iteratively, choosing each new sample x heuristically on t...
Marcus R. Frean, Phillip Boyle
BMCBI
2006
98views more  BMCBI 2006»
14 years 11 months ago
Quantitative comparison of EST libraries requires compensation for systematic biases in cDNA generation
Background: Publicly accessible EST libraries contain valuable information that can be utilized for studies of tissue-specific gene expression and processing of individual genes. ...
Donglin Liu, Joel H. Graber
IROS
2009
IEEE
172views Robotics» more  IROS 2009»
15 years 5 months ago
Modeling mobile robot motion with polar representations
— This article compares several parameterizations and motion models for improving the estimation of the nonlinear uncertainty distribution produced by robot motion. In previous w...
Joseph Djugash, Sanjiv Singh, Ben Grocholsky
IROS
2007
IEEE
95views Robotics» more  IROS 2007»
15 years 5 months ago
Modeling affordances using Bayesian networks
— Affordances represent the behavior of objects in terms of the robot’s motor and perceptual skills. This type of knowledge plays a crucial role in developmental robotic system...
Luis Montesano, Manuel Lopes, Alexandre Bernardino...