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A visual bag of words method for interactive qualitative localization and mapping

Abstract : Localization for low cost humanoid or animal-like personal robots has to rely on cheap sensors and has to be robust to user manipulations of the robot. We present a visual localization and map-learning system that relies on vision only and that is able to incrementally learn to recognize the different rooms of an apartment from any robot position. This system is inspired by visual categorization algorithms called bag of words methods that we modified to make fully incremental and to allow a user-interactive training. Our system is able to reliably recognize the room in which the robot is after a short training time and is stable for long term use. Empirical validation on a real robot and on an image database acquired in real environments are presented.
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Submitted on : Tuesday, November 15, 2011 - 11:01:26 AM
Last modification on : Wednesday, May 11, 2022 - 12:06:05 PM
Long-term archiving on: : Friday, November 16, 2012 - 10:57:09 AM


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David Filliat. A visual bag of words method for interactive qualitative localization and mapping. International Conference on Robotics and Automation, 2007, Italy. pp.3921 - 3926, ⟨10.1109/ROBOT.2007.364080⟩. ⟨hal-00640996⟩



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