Abstract
This paper proposes a novel localization method based on a camera that can estimate the absolute position of a vehicle using a public online database of road signs. The estimated absolute position near a road sign is used to compensate the drift error of visual odometry (VO). In the first phase, the relative position between a road sign and a vehicle is estimated by matching a detected road sign image with the reference image from a public online database. Subsequently, the absolute position of the vehicle is calculated using the data from the database. Once the absolute position of the vehicle is estimated near a road sign, the current position of VO is updated to compensate the accumulated error. From a 24-km driving road test, it is validated that the proposed algorithm can estimate the absolute position of a vehicle within an error of 1.5 m. Moreover, a test of trajectory 3 km showed that it can maintain the drift error of VO within tens of meters. Our method is easy to be deployed, has low computation cost, and is accessible to a wide range of driving environments such as highways.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agarwal, P., Burgard, W. and Spinello, L. (2015). Metric localization using google street view. Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Hamburg, Germany.
Agrawal, M. and Konolige, K. (2006). Real-time localization in outdoor environments using stereo vision and inexpensive GPS. Proc. 18th Int. Conf. Pattern Recognition (ICPR'06), Hong Kong, China.
Dornhege, C. and Kleiner, A. (2006). Visual odometry for tracked vehicles. Proc. IEEE Int. Workshop on Safety, Security and Rescue Robotics (SSRR), Gaithersburg, Maryland, USA.
Durrant-Whyte, H. and Bailey, T. (2006). Simultaneous localization and mapping: Part I. IEEE Robotics & Automation Magazine 13, 2, 99–110.
Fuentes-Pacheco, J., Ruiz-Ascencio, J. and Rendon- Mancha, J. M. (2015). Visual simultaneous localization and mapping: A survey. Artificial Intelligence Review 43, 1, 55–81.
Geiger, A., Ziegler, J. and Stiller, C. (2011). StereoScan: Dense 3d reconstruction in real-time. Proc. IEEE Intelligent Vehicles Symp. (IV), Baden-Baden, Germany.
Howard, A. (2008). Real-time stereo visual odometry for autonomous ground vehicles. Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Nice, France.
Jang, C., Park, J. E. and Kim, Y. K. (2017). Compensating drift of mono-visual odometry using road direction sign database. Proc. IEEE 20th Int. Conf. Intelligent Transportation Systems (ITSC), Yokohama, Japan.
Jang, G., Lee, S. and Kweon, I. (2002). Color landmark based self-localization for indoor mobile robots. Proc. IEEE Int. Conf. Robotics and Automation, Washington, D.C., USA.
Karami, E., Prasad, S. and Shehata, M. (2017). Image matching using SIFT, SURF, BRIEF and ORB: Performance comparison for distorted images. arXiv: 1710.02726.
Kitt, B., Rehder, J., Chambers, A., Schönbein, M., Lategahn, H. and Singh, S. (2011). Monocular visual odometry using a planar road model to solve scale ambiguity. Proc. European Conf. Mobile Robots, Örebro, Sweden.
Korea Institute of Civil Engineering and Building Technology (KICT. (2016). Roadsign Management System, https://doi.org/www.roadsign.go.kr
Moré, J. J. (1978). The levenberg-marquardt algorithm: Implementation and theory. Numerical Analysis, 105–116.
Olson, E. (2011). AprilTag: A robust and flexible visual fiducial system. Proc. IEEE Int. Conf. Robotics and Automation, Shanghai, China.
Pagel, F. (2009). Robust monocular egomotion estimation based on an IEKF. Proc. Canadian Conf. Computer and Robot Vision, Kelowna, BC, Canada.
Radwan, N., Tipaldi, G. D., Spinello, L. and Burgard, W. (2016). Do you see the bakery? Leveraging geo-referenced texts for global localization in public maps. Proc. IEEE Int. Conf. Robotics and Automation (ICRA), Stockholm, Sweden.
Redmon, J., Diwala, S., Girshick, R. and Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA.
Rublee, E., Rabaud, V., Konolige, K. and Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. Proc. Int. Conf. Computer Vision, Barcelona, Spain.
Scaramuzza, D., Fraundorfer, F., Pollefeys, M. and Siegwart, R. (2009). Absolute scale in structure from motion from a single vehicle mounted camera by exploiting nonholonomic constraints. Proc. IEEE 12th Int. Conf. Computer Vision, Kyoto, Japan.
Scaramuzza, D. and Fraundorfer, F. (2011). Visual odometry [Tutorial]. IEEE Robotics & Automation Magazine 18, 4, 80–92.
Strelow, D. and Singh, S. (2004). Motion estimation from image and inertial measurements. The Int. J. Robotics Research 23, 12, 1157–1195.
Tao, Z., Bonnifait, P., Fremont, V. and Ibanez-Guzman, J. (2013). Lane marking aided vehicle localization. Proc. 16th Int. IEEE Conf. Intelligent Transportation Systems (ITSC 2013), The Hague, Netherlands.
Triggs, B., McLauchlan, P. F., Hartley, R. I. and Fitzgibbon, A. W. (1999). Bundle adjustment - A modern synthesis. Proc. Int. Workshop on Vision Algorithms, Corfu, Greece.
Zamir, A. R. and Shah, M. (2010). Accurate image localization based on google maps street view. Proc. European Conf. Computer Vision, Crete, Greece.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jang, C., Kim, YK. Drift Compensation of Mono-Visual Odometry and Vehicle Localization Using Public Road Sign Database. Int.J Automot. Technol. 20, 1245–1254 (2019). https://doi.org/10.1007/s12239-019-0116-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-019-0116-6