DOI QR코드

DOI QR Code

Lane Detection-based Camera Pose Estimation

차선검출 기반 카메라 포즈 추정

  • Jung, Ho Gi (Department of Automotive Engineering, Hanyang University) ;
  • Suhr, Jae Kyu (The Research Institute of Automotive Control and Electronics, Hanyang University)
  • 정호기 (한양대학교 자동차전자제어연구소) ;
  • 서재규 (한양대학교 미래자동차공학과)
  • Received : 2014.08.14
  • Accepted : 2015.05.29
  • Published : 2015.09.01

Abstract

When a camera installed on a vehicle is used, estimation of the camera pose including tilt, roll, and pan angle with respect to the world coordinate system is important to associate camera coordinates with world coordinates. Previous approaches using huge calibration patterns have the disadvantage that the calibration patterns are costly to make and install. And, previous approaches exploiting multiple vanishing points detected in a single image are not suitable for automotive applications as a scene where multiple vanishing points can be captured by a front camera is hard to find in our daily environment. This paper proposes a camera pose estimation method. It collects multiple images of lane markings while changing the horizontal angle with respect to the markings. One vanishing point, the cross point of the left and right lane marking, is detected in each image, and vanishing line is estimated based on the detected vanishing points. Finally, camera pose is estimated from the vanishing line. The proposed method is based on the fact that planar motion does not change the vanishing line of the plane and the normal vector of the plane can be estimated by the vanishing line. Experiments with large and small tilt and roll angle show that the proposed method outputs accurate estimation results respectively. It is verified by checking the lane markings are up right in the bird's eye view image when the pan angle is compensated.

Keywords

References

  1. R. Szeliski, Computer Vision: Algorithms and Applications, Springer-Verlag London Limited 2011.
  2. Caltech Calibration Toolbox, http://www.vision.caltech.edu/bouguetj/calib_doc, 2015.
  3. M. Bertozz, A. Broggi and A. Fascioli, "Stereo Inverse Perspective Mapping: Theory and Applications," Image and Vision Computing, Vol.16, No.8, pp.585-590, 1998. https://doi.org/10.1016/S0262-8856(97)00093-0
  4. M. B. de Paula, C. R. Jung and L. G. da Silveira Jr., "Automatic On-the-fly Extrinsic Camera Calibration of Onboard Vehicular Cameras," Expert Systems with Applications, Vol.41, Issue 4, Part 2, pp.1997-2007, 2014. https://doi.org/10.1016/j.eswa.2013.08.096
  5. G. Stein, O. Shachar, E. Belman, G. Hayon and I. Gat, Bundling of Driver Assistance Systems, US Patent No. 8254635, 2012.
  6. L. Mazzei, P. Medici and M. Panciroli, "A Lasers and Cameras Calibration Procedure for VIAC Multi-sensorized Vehicles," Intelligent Vehicles Symposium, pp.548-553, 2012.
  7. V. Babaee-Kashany and H. R. Pourreza, "Camera Pose Estimation in Soccer Scenes Based on Vanishing Points," IEEE International Symposium on Haptic Audio-Visual Environments and Games, pp.1-6, 2010.
  8. S. Li and Y. Hai, "Estimating Camera Pose from H-Pattern of Parking Lot," IEEE International Conference on Robotics and Automation, Anchorage, pp.3954-3959, 2010.
  9. A. Guiducci, "Camera Calibration for Road Applications," Computer Vision and Image Understanding, Vol.79, No.2, pp.250-266, 2000. https://doi.org/10.1006/cviu.2000.0857
  10. S. Nedevschi, C. Vancea, T. Marita and T. Graf, "Online Extrinsic Parameters Calibration for Stereovision Systems Used in Far-range Detection Vehicle Applications," IEEE Transactions on Intelligent Transportation Systems, Vol.8, No.4, pp.651-660, 2007. https://doi.org/10.1109/TITS.2007.908576
  11. D. Kim and H. Jung, "Road Surface Marking Detection for Sensor Fusion-based Positioning System," Transactions of KSAE, Vol.22, No.7, pp.107-116, 2014.
  12. J. K. Suhr, H. G. Jung, G. Li and J. Kim, "Mixture of Gaussians-based Background Subtraction for Bayer-pattern Image Sequences," IEEE Transactions on Circuits and Systems for Video Technology, Vol.21, No.3, pp.365-370, 2011. https://doi.org/10.1109/TCSVT.2010.2087810
  13. H. G. Jung, Lane Detection (LD)-based Pose Calibration, http://web.yonsei.ac.kr/hgjung/LD_based_pose_calibration.htm, 2015.

Cited by

  1. A Study on the Vehicle Detection and Tracking Using Forward Wide Angle Camera vol.26, pp.3, 2018, https://doi.org/10.7467/KSAE.2018.26.3.368