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CLC number: TP391.4

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Received: 2007-08-24

Revision Accepted: 2007-10-30

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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.12 P.2017~2020

http://doi.org/10.1631/jzus.2007.A2017


Visual odometry for road vehicles—feasibility analysis


Author(s):  SOTELO Miguel-angel, GARCÍ,A Roberto, PARRA Ignacio, FERNÁ,NDEZ David, GAVILÁ,N Miguel, Á,LVAREZ Sergio, NARANJO José,-eugenio

Affiliation(s):  Department of Electronics, University of Alcalá more

Corresponding email(s):   sotelo@depeca.uah.es

Key Words:  3D visual odometry, Ego-motion estimation, RAndom SAmple Consensus (RANSAC), Photogrametric approach


SOTELO Miguel-angel, GARCÍA Roberto, PARRA Ignacio, FERNÁNDEZ David, GAVILÁN Miguel, ÁLVAREZ Sergio, NARANJO José-eugenio. Visual odometry for road vehicles—feasibility analysis[J]. Journal of Zhejiang University Science A, 2007, 8(12): 2017~2020.

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author="SOTELO Miguel-angel, GARCÍA Roberto, PARRA Ignacio, FERNÁNDEZ David, GAVILÁN Miguel, ÁLVAREZ Sergio, NARANJO José-eugenio",
journal="Journal of Zhejiang University Science A",
volume="8",
number="12",
pages="2017~2020",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A2017"
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Abstract: 
Estimating the global position of a road vehicle without using GPS is a challenge that many scientists look forward to solving in the near future. Normally, inertial and odometry sensors are used to complement GPS measures in an attempt to provide a means for maintaining vehicle odometry during GPS outage. Nonetheless, recent experiments have demonstrated that computer vision can also be used as a valuable source to provide what can be denoted as visual odometry. For this purpose, vehicle motion can be estimated using a non-linear, photogrametric approach based on RAndom SAmple Consensus (RANSAC). The results prove that the detection and selection of relevant feature points is a crucial factor in the global performance of the visual odometry algorithm. The key issues for further improvement are discussed in this letter.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Agrawal, M., Konolige, K., 2006. Real-time Localization in Outdoor Environments Using Stereo Vision and Inexpensive GPS. 18th Int. Conf. on Pattern Recognition, p.1063-1068.

[2] Alonso, I.P., Llorca, D.F., Sotelo, M.A., Bergasa, L.M., de Toro, P.R., Nuevo, J., Ocana, M., Garrido, M.A.G., 2007. Combination of feature extraction methods for SVM pedestrian detection. IEEE Trans. on Intell. Transport. Syst., 8(2):292-307.

[3] Fischler, M.A., Bolles, R.C., 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6):381-395.

[4] García, R., Sotelo, M., Parra, I., Fernández, D., Gavilán, M., Álvarez, S., Naranjo, J.E., 2007. Visual odometry for road vehicles. J. Intell. Rob. Syst., in press.

[5] Hagnelius, A., 2005. Visual Odometry. Master Thesis, Umea University.

[6] Nister, D., Naroditsky, O., Beren, J., 2004. Visual Odometry. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 1:652-659.

[7] Schmid, C., Mohr, R., Bauckhage, C., 2000. Evaluation of interest point detectors. Int. J. Computer Vision, 37(2):151-172.

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