Traversing seen and unseen corridors with Artificial Neural Networks/Context matching

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Video in TIB AV-Portal: Traversing seen and unseen corridors with Artificial Neural Networks/Context matching

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Traversing seen and unseen corridors with Artificial Neural Networks/Context matching
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CC Attribution 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Release Date
2011
Language
Silent film
Production Year
2011
Production Place
Dortmund

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Abstract
Proof of concept experiments to navigate a corridor environment using Two level feature matching architecture. Layer 1 classifies the scenario from the shape and appearance of the environment into Corridor (C) or Open room (O) or Cluttered (L) environment. Layer 2 deploys scenario specific model to predict action and correspondingly navigate the environment. The two models used are: 1. Artificial Neural Networks and 2. Context matching and prediction. Timeline: 0:00 Scenario: Known corridor with Artificial Neural Networks 0:17 Scenario: Known corridor with Context matching and prediction 0:39 Scenario: Unknown corridor with Artificial Neural Networks 1:02 Scenario: Unknown corridor with Context matching and prediction 1:18 Scenario: Transition between trained and an untrained corridor using Artificial Neural Networks This video is the supplement to the paper: "Scenario and context specific visual robot behavior learning" presented at the 2011 IEEE International Conference on Robotics and Automation (ICRA2011), May 9-13 2011, Shanghai International Convention Center, Shanghai, China. For more information please visit: http://www.rst.e-technik.tu-dortmund....
Keywords navigation gaussian mixture model learning from demonstration visual behaviors obstacle avoidance homing
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