본문 바로가기
KERD
논문KCI 등재

신호세기를 이용한 2차원 레이저 스캐너 기반 노면표시 분류 기법

Road marking classification method based on intensity of 2D Laser Scanner

0

조회

0

다운로드

0

인용

0

참고문헌

키워드

초록

With the development of autonomous vehicle, there has been active research on advanced driver assistance system for road marking detection using vision sensor and 3D Laser scanner. However, vision sensor has the weak points that detection is difficult in situations involving severe illumination variance, such as at night, inside a tunnel or in a shaded area; and that processing time is long because of a large amount of data from both vision sensor and 3D Laser scanner. Accordingly, this paper proposes a road marking detection and classification method using single 2D Laser scanner. This method road marking detection and classification based on accumulation distance data and intensity data acquired through 2D Laser scanner. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 3D Laser scanner-based method, thus demonstrating the possibility of road marking type classification using single 2D Laser scanner.

Abstract

With the development of autonomous vehicle, there has been active research on advanced driver assistance system for road marking detection using vision sensor and 3D Laser scanner. However, vision sensor has the weak points that detection is difficult in situations involving severe illumination variance, such as at night, inside a tunnel or in a shaded area; and that processing time is long because of a large amount of data from both vision sensor and 3D Laser scanner. Accordingly, this paper proposes a road marking detection and classification method using single 2D Laser scanner. This method road marking detection and classification based on accumulation distance data and intensity data acquired through 2D Laser scanner. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 3D Laser scanner-based method, thus demonstrating the possibility of road marking type classification using single 2D Laser scanner.

참고문헌 (15)

  1. M. Kang, "Map Building based on Sensor Fusion for Autonomous Vehicle", Proceedings of IEEE 2014Information Technology : New Generations, pp.490~495 (2014)
  2. 임성혁, "LRF (Laser Range Finder) 거리와 반사도를 이용한 보행자 보호용 노면표시 검출기법 연구", 제어.로봇.시스템학회 논문지, 18(1), pp.62~68 (2012), 제어·로봇·시스템학회
  3. T. Li, "A New 3D LIDAR-based Lane Markings Recognition Approach", International Conference on Robotics and Biomimetics, pp.2197~2202 (2013)
  4. D. An, "Detection of driving lane using road lane and mark information", 석사, pp.2010
  5. H. Guan, "Using mobile laser scanning data for automated extraction of road markings", ISPRS Journal of Photogrammetry and Remote Sensing, pp.93~107 (2014)
  6. F. Blais, "Range error analysis of an integrated time-of-flight, triangulation, and photogrammetric 3D laser scanning system", Proceedings of SPIE Aero Sense, 4035 (2000)
  7. J. Hancock, "Laser intensity based obstacle detection", Proceedings of the Intelligent Robots and System, 3, pp.1541~1546 (1998)
  8. M. Lee, "Obstacle Classification Method using Multi-Feature based on 2D LIDAR", 석사, pp.2015
  9. I. Oh, "Computer Vision", Hanbitacademy, ~ (2014)
  10. N. Otsu, "A Threshold Selection Method from Gray-Level Histograms", Transactions on Systems, MAN, and Cybernetics, SMC-9(1), pp.62~66 (1979)
  11. O. Hadjiliadis, "Sequential classification in point cloud of urban scenes", Proceedings of 3DPVT (2010)
  12. I. Oh, "Pattern Recognition", Kyobobook, ~ (2008)
  13. Y. Bin, "Image Stabilization by Combining Gray-Scale Projection and Block Matching Algorithm", Proceedings of IT in Medicine & Education, 1, pp.1262~1266 (2009)
  14. K. Briechle, "Template Matching using Fast Normalized Cross Correlation", Proceedings of SPIE, 4387, pp.95~102 (2001)
  15. D. M. Tsai, "Fast normalized cross correlation for defect detection", Pattern Recognition Letters, 24(15), pp.2625~2631 (2003)

관련 논문AI추천

이 간행물의 다른 논문 (6건)