Vehicle ego-localization by matching in-vehicle camera images to an aerial image

Authors:
Masafumi Noda;Tomokazu Takahashi;Daisuke Deguchi;Ichiro Ide;Hiroshi Murase;Yoshiko Kojima;Takashi Naito
Affiliations:
Nagoya University, Nagoya, Aichi, Japan;Nagoya University, Nagoya, Aichi, Japan and Gifu Shotoku Gakuen University, Gifu, Japan;Nagoya University, Nagoya, Aichi, Japan;Nagoya University, Nagoya, Aichi, Japan;Nagoya University, Nagoya, Aichi, Japan;Toyota Central Research & Development Laboratories, Inc., Nagakute, Aichi, Japan;Toyota Central Research & Development Laboratories, Inc., Nagakute, Aichi, Japan
Venue:
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Year:
2010

Citing 4
Cited 0

On map-matching vehicle tracking data

VLDB '05 Proceedings of the 31st international conference on Very large data bases
Space-time Analysis of Spherical Projection Image

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Map-Enhanced UAV Image Sequence Registration

WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
Speeded-Up Robust Features (SURF)

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

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.