Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Signal Compensation of LiDAR Sensors and Noise Filtering

LiDAR 센서 신호 보정 및 노이즈 필터링 기술 개발

  • Park, Hong-Sun (Department of Electrical Engineering, Chonnam National University) ;
  • Choi, Joon-Ho (Department of Electrical Engineering, Chonnam National University)
  • 박홍순 (전남대학교 전기공학과) ;
  • 최준호 (전남대학교 전기공학과)
  • Received : 2019.09.24
  • Accepted : 2019.09.30
  • Published : 2019.09.30
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Abstract

In this study, we propose a compensation method of raw LiDAR data with noise and noise filtering for signal processing of LiDAR sensors during the development phase. The raw LiDAR data include constant errors generated by delays in transmitting and receiving signals, which can be resolved by LiDAR signal compensation. The signal compensation consists of two stage. First one is LiDAR sensor calibration for a compensation of geometric distortion. Second is walk error compensation. LiDAR data also include fluctuation and outlier noise, the latter of which is removed by data filtering. In this study, we compensate for the fluctuation by using the Kalman filter method, and we remove the outlier noise by applying a Gaussian weight function.

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References

  1. B. Douillard, D. Fox and F. Ramos, "A spatio-temporal probabilistic model for multi-sensor multi-class object recognition," - Proc. 2007, IEEE/ RSJ Int. Conf. on Intell. Robot. Syst., pp. 2402-2408 San Diego, USA, 2007.
  2. L. Spinello and R. Suegwart, "Human detection using multimodal and multidimensional features", IEEE Int. Conf. on Robot. Autom., pp. 3264-3269, Pasadena, USA, 2008.
  3. C. Premebida, O. Ludwig, and U. Nunes, "Lidar and vision-based pedestrian detection system", J. Field Robot., Vol. 26, No. 9, pp. 696-711, 2009. https://doi.org/10.1002/rob.20312
  4. P. Palojarvi, "Integrated Electronic and opto-electronic circuits and devices for pulsed time-of-flight laser range finding", University of Oulu,Finland, 2003.
  5. A. Kilpela and J. Kostamovaara, "A laser pulser for a TOF laser radar", Rev. Sci. Instrum., Vol. 68, No. 6, pp. 2253-2258, 1997. https://doi.org/10.1063/1.1148133
  6. M. T. Thompson and M. F. Schlecht, "High Power Laser Diode Driver Based on Power Converter Technology", IEEE Trans. Power Electron., Vo1. l2, No. 1, pp. 46-52, 1997.
  7. Q. Wang, X.-J. Tian, G. Wu, and M.-Y. Luo, "Design of highpeak current and narrow pulse driver of laser diode", J. China Univ. Posts and Telecommun., Vol. 16, pp. 82-85, 2009. https://doi.org/10.1016/S1005-8885(08)60343-X
  8. A. Kilpela, "Pulsed time-of-flight laser ranfe finder techniques for fast, high precision measurement applications", University of Oulu, Finland, 2004.
  9. A. Kilpela, R. Pennala, and J. Kostamovaara, "Precise pulsed time-of-flight laser range finder for industrial distance measurements", Rev. Sci. Instrum., Vol. 72, No. 4, pp. 2197-2202, 2001. https://doi.org/10.1063/1.1355268
  10. S. J. Julier and J. K. Uhmann, "A new extension of the Kaman filter to nonlinear systems", Int. Symp. Aerosp. Def. Sens. Simul. Control., Vol. 3, No. 26, pp. 182-193 1997.
  11. E. A. Wan and R. Van Der Merwe, "The unscented Kalman filter for nonlinear estimation", Proc. IEEE 2000 Adapt. Syst. Signal Process. Commun. Control Symp., pp. 153-158, Lake Louise, Canada, 2000.
  12. G. Evensen, "The ensemble Kalman filter: Theoretical formulation and practical implementation", Ocean Dyn., Vol. 53, No. 4, pp. 343-367, 2000. https://doi.org/10.1007/s10236-003-0036-9
  13. K. Ito and K. Xiong, "Gaussian filters for nonlinear filtering problems", IEEE Trans. Autom. Control, Vol. 45, No. 5, pp. 910-927, 2000. https://doi.org/10.1109/9.855552
  14. G. Deng and L. W. Cahill, "An adaptive Gaussian filter for noise reduction and edge detection", IEEE Nucl. Sci. Symp. Med. Imaging Conf., Vol. 3, pp 1615-1619, 1993.
  15. G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer, "KNN model-based approach in classification", OTM Confed. Int. Conf. CoopIS DOA ODBASE 2003, pp. 986-996, Catania, Italy, 2003.
  16. Y. Wang, W. L. Chao, D. Garg, B. Hariharan, M. Campbell, & K. Q. Weinberger, "Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving", IEEE Conf, pp. 8445-8453, 2019.
  17. J. Zhang, and S. Sanjiv, "LOAM: Lidar Odometry and Mapping in Real-time", Robot. Sci. Syst., Vol. 2, 2014.