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Analysis of Traversable Candidate Region for Unmanned Ground Vehicle Using 3D LIDAR Reflectivity

3D LIDAR 반사율을 이용한 무인지상차량의 주행가능 후보 영역 분석

  • Received : 2017.03.20
  • Accepted : 2017.08.23
  • Published : 2017.11.01

Abstract

The range data acquired by 2D/3D LIDAR, a core sensor for autonomous navigation of an unmanned ground vehicle, is effectively used for ground modeling and obstacle detection. Within the ambiguous boundary of a road environment, however, LIDAR does not provide enough information to analyze the traversable region. This paper presents a new method to analyze a candidate area using the characteristics of LIDAR reflectivity for better detection of a traversable region. We detected a candidate traversable area through the front zone of the vehicle using the learning process of LIDAR reflectivity, after calibration of the reflectivity of each channel. We validated the proposed method of a candidate traversable region detection by performing experiments in the real operating environment of the unmanned ground vehicle.

무인지상차량의 자율주행을 위한 핵심센서로 사용되는 2D/3D 라이다(LIDAR) 센서에서 제공되는 거리 데이터는 지면 모델링 및 장애물 검출을 위해 효과적으로 활용되지만, 도로의 경계가 모호한 환경 등에서는 주행가능영역에 대한 분석이 어렵게 된다. 본 논문에서는 라이다의 반사율 특성을 이용하여 무인차량의 주행 가능한 영역에 대한 후보 영역을 추가로 분석함으로써 보다 효과적으로 주행가능영역을 검출할 수 있는 기법을 제안하였다. 3D LIDAR의 반사율을 보정하고 무인차량 전방 영역의 반사율에 대한 학습을 통해 주행가능 후보영역을 검출하였으며, 무인차량 실제 운용환경에서의 실험을 통해 후보영역 검출 결과를 검증하였다.

Keywords

References

  1. Choi, T. S., Shim, S. D. and Min, J. H., 2016, "A Study of 3D LIDAR based-Obstacle and Ground Detection Algorithm for Unmanned Ground Vehicles driving on Rough Terrain," Conference of the Korean Society of Mechanical Engineers, pp. 2824-2829.
  2. Kim, J., Min, J. H., Kwak, K. H. and Bae K. S., 2017, "Traversable Region Detection based on a Lateral Slope Feature for Autonomous Driving of UGVs," Journal of Institute of Control, Robotics and System, Vol 23. No 2, pp. 67-75. https://doi.org/10.5302/J.ICROS.2017.16.0204
  3. Operating Instructions, Laser Measurement System of the LMS500 Product Family (www. sick.com).
  4. LiDAR Comparison chart_Rev-A_2_Web, (www. velodynelidar.com).
  5. Hata, A. and Wolf, D., 2014, "Road Marking Detection using LIDAR Reflective Intensity Data and its Application to Vehicle Localization," IEEE International Conference on Intelligent Transportation System(ITSC), pp. 584-589.
  6. Ahn, S. Y., Min, J. H. and Kim, J., 2016, "A Lane Detection Method for Unmanned Ground Vehicle Using Reflectivity of 3D LIDAR," Conference of the Korean Society of Mechanical Engineers, pp. 2811-2815.
  7. Min, J. H., Choe, J. H. and Kwak, K. H., 2016, "A Study of 3D LIDAR based-Localization Algorithm for Unmanned Ground Vehicle," Conference of the Korean Society of Mechanical Engineers, pp. 2816-2819.
  8. Sugerm, B., Steder, B. and Burgard, W., 2016, "Terrain-adaptive Obstacle Detection," 2016 IEEE/ RSJ International Conference on Intelligent Robots and Systems(IROS), Daejeon, pp. 3608-3613.
  9. Jung, T. K., Song, J. H., Im, J. H., Lee, B. H. and Jee, G. I., 2016, "Localization using 3D-Lidar Based Road Reflectivity Map and IPM Image," Journal of Institute of Control Robotics and Systems(ICROS), Vol. 22, No 12, pp. 1061-1067. https://doi.org/10.5302/J.ICROS.2016.16.0173
  10. Levinson, J. and Thrun, S., 2010, "Robust Vehicle Localization in Urban Environment Using Probability Maps," IEEE International Conference on Robotics and Automation(ICRA), pp. 4372-4378.
  11. Sock, J. I., Kim, J., Min, J. H. and Kwak, K. H., 2016, "Probability Traversability Map Generation using 3D-LIDAR and Camera," IEEE International Conference on Robotics and Automation(ICRA), pp. 5631-5637.