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stereo data

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Self-supervised free space estimation in outdoor terrain
Ali Harakeh, Daniel Asmar, Elie Shammas
Journal:

Robotica / Volume 36 / Issue 9 / September 2018

Published online by Cambridge University Press:

01 June 2018, pp. 1278-1300
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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.