Adaptive unstructured road detection using close range stereo vision

Date

2013-06-26

Author

ÖZÜTEMİZ, Kadri Bugra
Hacinecipoglu, AKİF
Koku, Ahmet Buğra
Konukseven, Erhan İlhan

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Detection of road regions is not a trivial problem especially in unstructured and/or off-road domains since traversable regions of these environments do not have common properties unlike urban roads or highways. In this paper a novel unstructured road detection algorithm that can continuously learn the road region is proposed. The algorithm gathers close-range stereovision data and uses this information to estimate the long-range road region. The experiments show that the algorithm gives satisfactory results even under changing light conditions.

Subject Keywords

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

URI

https://hdl.handle.net/11511/36471

DOI

https://doi.org/10.1109/ascc.2013.6606352

Collections

Department of Mechanical Engineering, Conference / Seminar

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Self-learning Road Detection with Stereo Vision Ozutemiz, Kadri Bugra; Hacinecipoglu, Akif; Koku, Ahmet Buğra; Konukseven, Erhan İlhan (2013-04-26) 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.
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Citation Formats

K. B. ÖZÜTEMİZ, A. Hacinecipoglu, A. B. Koku, and E. İ. Konukseven, “Adaptive unstructured road detection using close range stereo vision,” 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36471.