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City London / / Company Cambridge University Press / MIT Press / S. J. Maybank / / Country United States / United Kingdom / / Currency pence / USD / / / Facility Carnegie Mellon University / Stanford University / University of Wellington / Information Technologies The University of Sydney / / IndustryTerm visual odometry applications / machine learning algorithms / stand-alone solution / probabilistic tool / visual odometry algorithms / monocular visual odometry algorithms / real-time applications / aerial applications / loop-closure algorithm / ground vehicle applications / non-parametric tool / non-parametric algorithm / visual systems / conventional car / infinite neural networks / robotic car / gradient descent algorithms / structure-frommotion algorithm / / Organization Cambridge University / Visual Odometry Vitor Guizilini and Fabio Ramos Australian Centre for Field Robotics / MIT / University of Sydney / Stanford University / University of Wellington / Carnegie Mellon University / U.S. Securities and Exchange Commission / / Person Fabio Ramos / / ProgrammingLanguage L / / ProvinceOrState Minnesota / / PublishedMedium Machine Learning / / SportsLeague Stanford University / / Technology GPS system / Laser / non-parametric algorithm / machine learning algorithms / same ICP algorithm / monocular visual odometry algorithms / visual odometry algorithms / neural network / 7-point RANSAC algorithm / Selfcalibration algorithms / GPS / RANSAC algorithm / loop-closure algorithm / structure-frommotion algorithm / gradient descent algorithms / machine learning / SIFT algorithm / EM algorithm / filtered using the RANSAC algorithm / / SocialTag 
2012 IEEE International Conference on Robotics and Automation RiverCentre, Saint Paul, Minnesota, USA May 14-18, 2012 Semi-parametric Models for Visual Odometry Vitor Guizilini and Fabio Ramos