Mostrando entradas con la etiqueta autonomous robots. Mostrar todas las entradas
Mostrando entradas con la etiqueta autonomous robots. Mostrar todas las entradas

viernes, 18 de mayo de 2012

Entropy will find the way

The most awesome measure in Information Theory is entropy. It is widely used in pattern recognition [Escolano,Suau,Bonev 2009] because it quantifies the expected value of the information contained in a message.
As far as I remember the first application I gave to entropy was to help a robot to find its way in a semi-structured environment using only vision [Bonev,Cazorla,Escolano 2007]. Not in terms of high-level knowledge, but just to go where there seem to be things in the distance. If people have a walk they don't get stuck by trying to go against a building. Instead, they see something in the end of a street and they start to walk that way. 
In an image representing 360º of the environment that "something in the end of a street" is visually perceived as a more entropic region:
To avoid ambiguities I forced to have only two most entropic regions at a time: the two ends of a corridor or a street. A Fourier approximation results in the following map: for each moment in the time line we have only to hot regions in the angle axis. The robot should head to one of them: the one which grees with its current heading.
This simplistic approach had to be aided by another vision-based mechanism to avoid obstacles. I refer to it as visual sonars, but no range sensors and no GPS are used in the following video, only vision:


[Escolano, Suau, Bonev 2009] F. Escolano, P. Suau, B. Bonev. "Information Theory in Computer Vision and Pattern Recognition". (Hardcover) Springer, 2009
[Bonev, Cazorla, Escolano 2007] B. Bonev, M. A. Cazorla, F. Escolano. "Robot Navigation Behaviors based on Omnidirectional Vision and Information Theory". Journal of Physical Agents - September 2007

martes, 1 de noviembre de 2011

Information Theory in Computer Vision and Mobile Robotics


Information Theory is both a set of tools and a theoretical framework for many pattern recognition problems, one of them is Computer Vision. The following slides are from a talk I gave at the Max Planck Institute in Tübingen in 2010. They are a picture of the uses and significance of Information Theory in Computer Vision, mainly in the context of our research at the Dept. of Computer Science and Artificial Intelligence in Alicante.
Information-theoretic Computer Vision for Autonomous Robots

Pdf is also available.