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4. The Autonomous City Explorer

This video was recorded at AAAI 2009: AI Video Competition. This video presents the Autonomous City Explorer (ACE) project. Its goal was to create a robot capable of navigating in an unknown urban environments without the use of prior map knowledge or GPS data. The robot had to find its way solely by interacting with pedestrians and building a topological representation of its surroundings. This video outlines the necessary ingredients for successful low-level navigation on sidewalks, vision processing in cluttered outdoor environments, and information retrieval from pedestrians. More information can be found at http://www.ace-robot.de.

Keywords:: videolectures, ocwc, oec

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Science and Technology / Computer Science

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Material Type:: Presentation
Date Added to MERLOT:: February 10, 2015
Date Modified in MERLOT:: February 10, 2015
Authors:: Martin Buss, Institute of Automatic Control Engineering, TU Munich
Kolja Kühnlenz, Institute of Automatic Control Engineering, TU Munich
Dirk Wollherr, Institute of Automatic Control Engineering, TU Munich
Florian Rohrmüller, Institute of Automatic Control Engineering, TU Munich
Tianguang Zhang, Institute of Automatic Control Engineering, TU Munich
Quirin Mühlbauer, Institute of Automatic Control Engineering, TU Munich
Georgios Lidoris, Institute of Automatic Control Engineering, TU Munich
Stefan Sosnowski, Institute of Automatic Control Engineering, TU Munich
Tingting Xu, Institute of Automatic Control Engineering, TU Munich
Klaas Klasing, Institute of Automatic Control Engineering, TU Munich
Andrea Bauer, Institute of Automatic Control Engineering, TU Munich
Submitter:: The Open Education Consortium
Primary Audience:: College General Ed, College Lower Division, College Upper Division
Technical Format:: Video

Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Accessibility Information Available:: Unknown
Creative Commons:: This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States