Browse > Article
http://dx.doi.org/10.7319/kogsis.2017.25.2.021

Development of a Vehicle Positioning Algorithm Using In-vehicle Sensors and Single Photo Resection and its Performance Evaluation  

Kim, Ho Jun (Department of Geoinformatics, University of Seoul)
Lee, Im Pyeong (Department of Geoinformatics, University of Seoul)
Publication Information
Journal of Korean Society for Geospatial Information Science / v.25, no.2, 2017 , pp. 21-29 More about this Journal
Abstract
For the efficient and stable operation of autonomous vehicles or advanced driver assistance systems being actively studied nowadays, it is important to determine the positions of the vehicle accurately and economically. A satellite based navigation system is mainly used for positioning, but it has a limitation in signal blockage areas. To overcome this limitation, sensor fusion methods including additional sensors such as an inertial navigation system have been mainly proposed but the high sensor cost has been a problem. In this work, we develop a vehicle position estimation algorithm using in-vehicle sensors and a low-cost imaging sensor without any expensive additional sensor. We determine the vehicle positions using the velocity and yaw-rate of a car from the in-vehicle sensors and the position and attitude of the camera based on the single photo resection process. For the evaluation, we built a prototype system, acquired test data using the system, and estimated the trajectory. The proposed algorithm shows the accuracy of about 40% higher than an in-vehicle sensor only method.
Keywords
In-vehicle Sensor; Position and Attitude Determination; Trajectory Estimation; Single Photo Resection; Accuracy Evaluation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Bayoud, F. A., 2005, Vision-aided inertial navigation using a geomatics approach, Proc. of ION GNSS-2005, IEEE, Long Beach, USA, pp. 2485-2493.
2 Byun, Y. S., Mok, J. K. and Kim, Y. C., 2011, Kinematic model of 4ws vehicle for dead-reckoning, Proc. of the Information and Control Symposium, CICS, Gyeongju, Korea, pp. 360-361.
3 Chang, H., Georgy, J. and El-Sheimy, N., 2013, Monitoring Cycling Performance Using a Low Cost Multi-Sensors Navigation Solution, Proc. of the 8th The International Symposium on Mobile Mapping Technology, ISPRS, Tainan, Taiwan.
4 Dissanayake, G., Sukkarieh, S. and Nebot, E. and Durrant-Whyte, H., 2001, The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications, IEEE Transactions on Robotics and Automation, Vol. 17, No. 5, pp. 731-747.   DOI
5 Franke, U., Pfeiffer, D., Rabe, C., Knoeppel, C., Enzweiler, M., Stein, F. and Herrtwich, R., 2013, Making bertha see, Proc. of the IEEE International Conference on Computer Vision Workshops, IEEE, Sydney, Australia, pp. 214-221.
6 Geiger, A., Lenz, P., Stiller, C. and Urtasun, R., 2013, Vision meets robotics: The KITTI dataset, The International Journal of Robotics Research, Vol. 32, No. 11, pp. 1231-1237.   DOI
7 Guizzo, E., 2011, How google's self-driving car works, IEEE Spectrum Online, http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self-driving-car-works
8 Habib, A., Asmamaw, A., Kelley, D. and May, M., 2000, Linear features in photogrammetry, Research report, Department of civil and Environmental Engineering and Geodetic Science, Ohio State University, USA, pp. 9-11.
9 Jo, K. C., Chu, K. Y., Lee, K. Y. and Sunwoo, M. H., 2010, Integration of multiple vehicle models with an IMM filter for vehicle localization, Proc. of the 2010 IEEE Intelligent Vehicles Symposium, IEEE, San Diego, California, pp. 746-751.
10 Kim, S. B., Bazin, J. C., Lee, H. K., Choi, K. H. and Park, S. Y., 2011, Ground vehicle navigation in harsh urban conditions by integration inertial navigation system, global positioning system, odometer and vision data, IET radar, sonar&navigation, Vol. 5, No. 8, pp. 814-823.   DOI
11 Kim, H. J., Choi, K. A. and Lee, I. P., 2013, Development and evaluation of a system to determine positions and attitudes using in-vehicle sensors, Journal of Korea Spatial Information Society, Vol. 21, No. 6, pp. 57-67.   DOI
12 Lothe, P., Bourgeois, S., Dekeyser, F., Royer, E. and Dhome, M., 2009, Towards geographical referencing of monocular slam reconstruction using 3d city models: Application to real-time accurate vision-based localization, Proc. of the CVPR 2009, IEEE, Miami, USA, pp. 2882-2889.
13 Milford, M. and Wyeth, G., 2010, Persistent navigation and mapping using a biologically inspired SLAM system, The International Journal of Robotics Research, Vol. 29, No. 9, pp. 1131-1153.   DOI
14 Pink, O., 2008, Visual map matching and localization using a global feature map, Proc. of the CVPRW 2008, IEEE, Alaska, USA, pp. 1-7.
15 Royer, E., Lhuillier, M., Dhome, M. and Lavest, J. M., 2007, Monocular vision for mobile robot localization and autonomous navigation, International Journal of Computer Vision, Vol. 74, No. 3, pp. 237-260.   DOI
16 Scaramuzza, D and Fraundorfer, F., 2012, Visual odometry [tutorial], Robotics & Automation Magazine, IEEE, Vol. 18, No. 4, pp. 80-92.
17 Ziegler, J., Bender, P., Schreiber, M., Lategahn, H., Strauss, T., Stiller, C., Dang, T., Franke, U., Appenrodt, N., Keller, C.G. and Kaus, E., 2014, Making bertha drive-An Autonomous Journey on a Historic Route, Intelligent Transportation Systems Magazine, Vol. 6, No. 2, pp. 8-20.
18 Soloviev, A. and Venable, D., 2010, Integration of GPS and vision measurements for navigation in GPS challenged environments, Proc. of Position Locaion and Navigation Symposium, IEEE, California, USA, pp. 826-833.