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Self-supervised free space estimation in outdoor terrain

Published online by Cambridge University Press:  01 June 2018

Ali Harakeh ,
Daniel Asmar  and
Elie Shammas
Ali Harakeh
Affiliation:
VRL Lab, Department of Mechanical Engineering, American University of Beirut, Riad El-Solh, 1107 2020 Beirut, Lebanon. E-mails: [email protected], [email protected]
Daniel Asmar*
Affiliation:
VRL Lab, Department of Mechanical Engineering, American University of Beirut, Riad El-Solh, 1107 2020 Beirut, Lebanon. E-mails: [email protected], [email protected]
Elie Shammas
Affiliation:
VRL Lab, Department of Mechanical Engineering, American University of Beirut, Riad El-Solh, 1107 2020 Beirut, Lebanon. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

The ability to reliably estimate free space is an essential requirement for efficient and safe robot navigation. This paper presents a novel system, built upon a stochastic framework, which estimates free space quickly from stereo data, using self-supervised learning. The system relies on geometric data in the close range of the robot to train a second-stage appearance-based classifier for long range areas in a scene. Experiments are conducted on board an unmanned ground vehicle, and the results demonstrate the advantages of the proposed technique over other self-supervised systems.

Keywords

Free space estimationStereo dataSelf-supervised learningOutdoor terrain
Type
Articles
Information
Robotica , Volume 36 , Issue 9 , September 2018 , pp. 1278 - 1300
Copyright
Copyright © Cambridge University Press 2018 

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