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Development of a General Purpose Simulator for Evaluation of Vehicle LIDAR Sensors and its Application

차량용 라이다 센서의 평가를 위한 범용 시뮬레이터 개발 및 적용

  • Im, Ljunghyeok (Department of Geoinformatics, University of Seoul) ;
  • Choi, Kyongah (Department of Geoinformatics, University of Seoul) ;
  • Jeong, Jihee (Department of Geoinformatics, University of Seoul) ;
  • Lee, Impyeong (Department of Geoinformatics, University of Seoul)
  • 임륭혁 (서울시립대학교 공간정보공학과) ;
  • 최경아 (서울시립대학교 공간정보공학과) ;
  • 정지희 (서울시립대학교 공간정보공학과) ;
  • 이임평 (서울시립대학교 공간정보공학과)
  • Received : 2015.06.08
  • Accepted : 2015.06.24
  • Published : 2015.06.30

Abstract

In the development of autonomous vehicles, the importance of LIDAR sensors becomes larger. For sensor selection or algorithm development, it is difficult to test expensive LIDAR sensors mounted on a vehicle under various driving environment. In this study, we developed a simulator that is generally applicable for various vehicle LIDAR sensors based on the generalized geometric modeling of the common processes associated with vehicle LIDAR sensors. By configuring this simulator with the specific sensors being widely used, we performed the data simulation and quality analysis. Also, we applied the simulation data to obstacle detection and evaluated the applicability of the selected sensor. The developed simulator enables various experiments and algorithm development in parallel with hardware implementation prior to the deployment and operation of a sensor.

자율 주행 자동차 개발에 있어서 라이다 센서의 중요성이 커지고 있다. 센서 선정이나 알고리즘 개발을 위해 고가의 라이다 센서를 차량에 탑재하여 다양한 주행 환경에 대해 시험하기에 어려움이 따른다. 이에 본 연구는 다양한 차량용 라이다 센서에 대한 일반화된 기하모델링을 통해 범용적으로 적용될 수 있는 차량용 라이다 시뮬레이터를 개발하였다. 개발된 시뮬레이터를 활용하여 많이 활용되고 있는 특정 센서에 대하여 데이터 시뮬레이션과 품질 검증을 수행하였다. 또한, 생성된 데이터를 장애물 탐지에 적용함으로써 선정된 센서의 활용 가능성을 평가하였다. 이처럼 개발된 시뮬레이터는 센서의 도입 및 운용에 앞서서 다양한 실험을 가능하게 하고, 하드웨어 구축과 병행하여 알고리즘 개발을 도모할 수 있다.

Keywords

References

  1. Ahn, S., Y. Choe, and M.J. Chung, 2012. Fast Scene Understanding in Urban Environments for an Autonomous Vehicle equipped with 2D Laser Scanners, The Journal of Korea Robotics Society, 7(2): 92-100 (in Korean with an English abstract). https://doi.org/10.7746/jkros.2012.7.2.092
  2. Atanacio-Jimenez, G., J.-J. Gonzalez-Barbosa, J.B. Hurtado-Ramos, F.J. Ornelas-Rodriguez, H. Jimenez-Hernandez, T. Garcia-Ramirez, and R. Gonzalez-Barbosa, 2011. Lidar Velodyne HDL-64E Calibration using Pattern Planes, International Journal of Advanced Robotic Systems, 8.5: 70-82. https://doi.org/10.5772/45709
  3. Blanquer, E., 2007. LADAR proximity fuze-system study, Master's Degree Project Stockholm, Sweden.
  4. Chun, C.-M., S.B. Suh, S.H. Lee, C.W. Roh, S.C. Kang,and Y.S. Kang, 2010. Autonomous Navigation of KUVE (KIST Unmanned Vehicle Electric), Journal of Institute of Control, Robotics and Systems, 16(7): 617-624. https://doi.org/10.5302/J.ICROS.2010.16.7.617
  5. Glaser, C., T.P. Michalke, L. Burkle, F. Niewels, and R. Bosch, 2014. Environment perception for innercity driver assistance and highly-automated driving, Proc. of Intelligent Vehicles Symposium. June 8-11, Dearborn, Michigan, USA.
  6. Glennie, C., 2010. Static calibration and analysis of the Velodyne HDL-64E S2 for high accuracy mobile scanning, Remote Sensing, 2.6: 610-1624. https://doi.org/10.3390/rs2061610
  7. Hutchinson, B.A., R.L. Galbraith, B.L. Stann, and S.Z. Der, 2003. Simulation-based analysis of range and cross-range resolution requirements for the identification of vehicles in ladar imagery, Optical Engineering, 42(9): 2734-2745. https://doi.org/10.1117/1.1599837
  8. Kim, J., K. Jo, D. Kim, K. Chu, and M. Sunwoo, 2013. Behavior and Path Planning Algorithm of Autonomous Vehicle A1 in Structured Environments, Proc. of IFAC Intelligent Autonomous Vehicle Symposium, Sofitel Gold Coast, Gold Coast, Australia, pp.36-41.
  9. Kim, J., K.K. Kwon, and S.I. Lee, 2012. Trends and Applications on Lidar Sensor Technology, ETRI, Electronics and Telecommunications Trends (in Korean with an English abstract).
  10. Kim, S., S.H. Min, I.P. Lee, and K.A. Choi, 2008. Geometric Modeling and Data Simulation of an Airborne LIDAR System, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry abd Cartography, 26.3: 311-320 (in Korean with an English abstract).
  11. Lovell, J., D.L.B. Jupp, G. Newnham, N.C. Coops, and D.S. Culvenor, 2005. Simulation study for finding optimal lidar acquisition parameters for forest height retrieval, Forest Ecology and Management, 214(1): 398-412. https://doi.org/10.1016/j.foreco.2004.07.077
  12. Markoff, J., 2009, October, 9. Google Cars Drive Themselves, in Traffic, The New York Times.
  13. Miller, I., S. Lupashin, N. Zych, P. Moran, B. Schimpf, A. Nathan, and E. Garcia, 2006. Cornell University's 2005 DARPA grand challenge entry, Journal of Field Robotics, 23(8): 625-652. https://doi.org/10.1002/rob.20136
  14. Theisen, B. 2011. Autonomous Mobility Applique System (AMAS), Army Tank Automotive Research Development and Engineering Center, Warren MI.
  15. Thrun, S., M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. alpenny, G. Hoffmann, K. Lau, C. Oakley, M. Palatucci, V. Pratt, P. Stang, S. Strohband, C. Dupont, L. Jendrossek, C. Koelen, C. Markey, C. Rummel, J. van Niekerk, E. Jensen, P. lessandrini G. Bradski, B. Davies, S. Ettinger, A. Kaehler, A. Nefian, and P. Mahoney, 2006, Stanley: The robot that won the DARPA Grand Challenge, Journal of filed Robotics, 23(9): 661-692. https://doi.org/10.1002/rob.20147
  16. Tropschuh, P.F. and M. Biendl, 2015. Audi: Raw Materials, Road, Recycling-How Life Cycle Analysis Influences Product Development, Springer International Publishing, pp. 167-183.
  17. Vasile, A.N. and R.M. Marino, 2005. Pose-independent automatic target detection and recognition using 3D laser radar imagery, Lincoln Laboratory Journal, 15(1): 61-78.

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