Self-learning Road Detection with Stereo Vision

Date

2013-04-26

Author

Ozutemiz, Kadri Bugra
Hacinecipoglu, Akif
Koku, Ahmet Buğra
Konukseven, Erhan İlhan

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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It is a hard to solve problem to detect traversable or road regions especially in unstructured roads or paths. In mobile robot applications, robots usually enter these kinds of roads and regions. To successfully complete its mission, it is important to find roads in these environments reliably. In this paper a novel unstructured road detection algorithm with the capability of learning road regions continuously is proposed.

Subject Keywords

Near-to-far learning, Continuous learning, Traversibilit, Unstructured roads, Stereo vision, Road detection

URI

https://hdl.handle.net/11511/36715

DOI

https://doi.org/10.1109/siu.2013.6531314

Conference Name

21st Signal Processing and Communications Applications Conference (SIU)

Collections

Department of Mechanical Engineering, Conference / Seminar

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Citation Formats

K. B. Ozutemiz, A. Hacinecipoglu, A. B. Koku, and E. İ. Konukseven, “Self-learning Road Detection with Stereo Vision,” presented at the 21st Signal Processing and Communications Applications Conference (SIU), CYPRUS, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36715.