Abstract
Obtaining an accurate vehicle position is important for intelligent vehicles in supporting driver safety and comfort. This paper proposes an accurate ego-localization method by matching in-vehicle camera images to an aerial image. There are two major problems in performing an accurate matching: (1) image difference between the aerial image and the in-vehicle camera image due to view-point and illumination conditions, and (2) occlusions in the in-vehicle camera image. To solve the first problem, we use the SURF image descriptor, which achieves robust feature-point matching for the various image differences. Additionally, we extract appropriate feature-points from each road-marking region on the road plane in both images. For the second problem, we utilize sequential multiple in-vehicle camera frames in the matching. The experimental results demonstrate that the proposed method improves both ego-localization accuracy and stability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Google Inc.: Google Maps (2005), http://maps.google.com/
Brakatsoulas, S., Pfoser, D., Salas, R., Wenk, C.: On map-maching vehicle tracking data. In: Proc. 32nd Conf. on Very Large Data Bases, pp. 853–864 (2005)
Kawasaki, H., Miyamoto, A., Ohsawa, Y., Ono, S., Ikeuchi, K.: Multiple video camera calibration using EPI for city modeling. In: Proc. 6th Asian Conf. on Computer Vision, vol. 1, pp. 569–574 (2004)
Ono, S., Mikami, T., Kawasaki, H., Ikeuchi, K.: Space-time analysis of spherical projection image. In: Proc. 18th Int. Conf. on Pattern Recognition, pp. 975–979 (2006)
Uchiyama, H., Deguchi, D., Takahashi, T., Ide, I., Murase, H.: Ego-localization using streetscape image sequences from in-vehicle cameras. In: Proc. Intelligent Vehicle Symp. 2009, pp. 185–190 (2009)
Lin, Y., Yu, Q., Medioni, G.: Map-enhanced UAV image sequence registraton. In: Proc. 8th Workshop on Applications of Computer Vision, pp. 15–20 (2007)
Caballero, F., Luis Merino, J.F., Ollero, A.: Homography based Kalman filter for mosaic building. applications to UAV position estimation. In: Proc. Int. Conf. on Robotics and Automation, pp. 2004–2009 (2007)
Pink, O., Moosmann, F., Bachmann, A.: Visual features for vehicle localization and ego-motion estimation. In: Proc. Intelligent Vehicle Symp. 2009, pp. 254–260 (2009)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: SURF: Speeded up robust features. Computer Vision and Image Understanding 110, 346–359 (2008)
Applanix Corp.: POS LV (2009), http://www.applanix.com/products/land/pos-lv.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Noda, M. et al. (2011). Vehicle Ego-Localization by Matching In-Vehicle Camera Images to an Aerial Image. In: Koch, R., Huang, F. (eds) Computer Vision – ACCV 2010 Workshops. ACCV 2010. Lecture Notes in Computer Science, vol 6469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22819-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-22819-3_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22818-6
Online ISBN: 978-3-642-22819-3
eBook Packages: Computer ScienceComputer Science (R0)