Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Intensity Local Map Generation Using Data Accumulation and Precise Vehicle Localization Based on Intensity Map

데이터 누적을 이용한 반사도 지역 지도 생성과 반사도 지도 기반 정밀 차량 위치 추정

  • Received : 2016.09.02
  • Accepted : 2016.10.12
  • Published : 2016.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

For the safe driving of autonomous vehicles, accurate position estimation is required. Generally, position error must be less than 1m because of lane keeping. However, GPS positioning error is more than 1m. Therefore, we must correct this error and a map matching algorithm is generally used. Especially, road marking intensity map have been used in many studies. In previous work, 3D LIDAR with many vertical layers was used to generate a local intensity map. Because it can be obtained sufficient longitudinal information for map matching. However, it is expensive and sufficient road marking information cannot be obtained in rush hour situations. In this paper, we propose a localization algorithm using an accumulated intensity local map. An accumulated intensity local map can be generated with sufficient longitudinal information using 3D LIDAR with a few vertical layers. Using this algorithm, we can also obtain sufficient intensity information in rush hour situations. Thus, it is possible to increase the reliability of the map matching and get accurate position estimation result. In the experimental result, the lateral RMS position error is about 0.12m and the longitudinal RMS error is about 0.19m.

Keywords

References

  1. D. Kim, T. Jung, and K.-S. Yi, "Lane map-based vehicle localization for robust lateral control of an automated vehicle," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 21, no. 2, pp. 108-114, 2015. https://doi.org/10.5302/J.ICROS.2015.14.9005
  2. J. Levinson, J. Askeland, J. Dolson, and S. Thrun, "Traffic light mapping, localization, and state detection for autonomous vehicles," Proc. of Robotics and Automation (ICRA), 2011 IEEE International Conference on. IEEE, May 2011.
  3. A. Y. Hata, F. S. Osorio, and D. F. Wolf, "Robust curb detection and vehicle localization in urban environments," 2014 IEEE Intelligent Vehicles Symposium Proceedings. IEEE, 2014.
  4. R. W. Wolcott and R. M. Eustice, "Visual localization within lidar maps for automated urban driving," Proc. of 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2014.
  5. J. Levinson and S. Thrun, "Robust vehicle localization in urban environments using probabilistic maps," Proc. of Robotics and Automation (ICRA), 2010 IEEE International Conference on. IEEE, 2010.
  6. C.-C. Wang and C. Thorpe, "Simultaneous localization and mapping with detection and tracking of moving objects," Robotics and Automation, 2002. Proceedings. ICRA'02. IEEE International Conference on, vol. 3. IEEE, 2002.
  7. Thrun, Sebastian, and Michael Montemerlo. "The graph SLAM algorithm with applications to large-scale mapping of urban structures," The International Journal of Robotics Research, 25.5-6, pp. 403-429, 2006. https://doi.org/10.1177/0278364906065387
  8. Z. J. Chong, B. Qin, T. Bandyopadhyay, M. H. Ang, E. Frazzoli, and D. Rus, "Synthetic 2d lidar for precise vehicle localization in 3d urban environment," Proc. of Robotics and Automation (ICRA), 2013 IEEE International Conference on. IEEE, 2013.
  9. J. Levinson, M. Montemerlo, and S. Thrun, "Map-based precision vehicle localization in urban environments," Robotics: Science and Systems, vol. 4, 2007.

Cited by

  1. Precise Vehicle Position and Heading Estimation Using a Binary Road Marking Map vol.2019, pp.1687-7268, 2019, https://doi.org/10.1155/2019/1296175