센서학회지 (Journal of Sensor Science and Technology)

  • 제33권4호
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  • Pages.187-195
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  • 2024
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  • 1225-5475(pISSN)
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  • 2093-7563(eISSN)
한국센서학회 (The Korean Sensors Society)
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LiDAR Measurement Analysis in Range Domain

  • Sooyong Lee (Department of Mechancal and System Desgin Engineering, Hongik Unversity)
  • 투고 : 2024.06.27
  • 심사 : 2024.07.19
  • 발행 : 2024.07.31
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초록

Light detection and ranging (LiDAR), a widely used sensor in mobile robots and autonomous vehicles, has its most important function as measuring the range of objects in three-dimensional space and generating point clouds. These point clouds consist of the coordinates of each reflection point and can be used for various tasks, such as obstacle detection and environment recognition. However, several processing steps are required, such as three-dimensional modeling, mesh generation, and rendering. Efficient data processing is crucial because LiDAR provides a large number of real-time measurements with high sampling frequencies. Despite the rapid development of controller computational power, simplifying the computational algorithm is still necessary. This paper presents a method for estimating the presence of curbs, humps, and ground tilt using range measurements from a single horizontal or vertical scan instead of point clouds. These features can be obtained by data segmentation based on linearization. The effectiveness of the proposed algorithm was verified by experiments in various environments.

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과제정보

This research was supported in part by the Basic Research Project of Korea Institute of Machinery and Materials (Project ID: NK242I)

참고문헌

  1. K. Wang, N. Jiasheng, and L. Yanqiang, "A Robust LiDAR State Estimation and Map Building Approach for Urban Road", Proc. of 2021 IEEE 2nd Int. Conf. Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), pp. 502-506, Nanchang, China, 2021.
  2. K. Higashimoto, H. Fukushima, K. Kamitani, and N. Chujo, "Identification of Road Surface Condition on Undeveloped Roads : - Aiming for Remote Car Driving -", Proc. of 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE), pp. 777-781, Kyoto, Japan, 2021.
  3. J. Kim, K. Kwak, and K. Bae, "Experimental Analysis and Internal Calibration of the 3D LIDAR Reflectivity", J. Inst. Control Robot. Syst., Vol. 23, No. 7, pp. 574-582, 2017.
  4. J. Huang, P. K. Choudhury, S. Yin, and L. Zhu, "Real-Time road curb and lane detection for autonomous driving using LiDAR point clouds", IEEE Access, Vol. 9, pp. 144940-144951, 2021.
  5. M. Lee, M. H. Cha, H. Lee, and S. Lee, "Lidar Measurement Analysis for Detection of Down-Curbs and Up-Curbs", J. Inst. Control Robot. Syst., Vol. 29, No. 2, pp. 126-134, 2023.
  6. G. Wang, J. Wu, R. He, and B. Tian, "Speed and accuracy tradeoff for LiDAR data based road boundary detection", IEEE/CAA J. Autom. Sin., Vol. 8, No. 6, pp.1210-1220, 2021.
  7. G.-R. Rhee and J. Y. Kim, "A 3D map update algorithm based on removal of detected object using camera and lidar sensor fusion", J. Inst. Control Robot. Syst., Vol. 27, No. 11, pp. 883-889, 2021.
  8. J.-S. Lee, C. Ryu, and H. Kim, "Real-time object Detection based on Fusion of Vision and Point-cloud Interpolated LiDAR", J. Inst. Control Robot. Syst., Vol. 26, No. 6, pp. 469-478, 2020.
  9. M. Lee, J.-C. Kim, M. H. Cha, H. Lee, and S. Lee, "Identifying Puddles based on Intensity Measurement using LiDAR", J. Sens. Sci. Technol., Vol. 32, No. 5, pp. 262-274, 2023.
  10. R. Killick, P. Fearnhead, and I. A. Eckley, "Optimal detection of changepoints with a linear computational cost", J. Am. Stat. Assoc., Vol. 107, No. 500, pp. 1590-1598, 2012.
  11. M. Lavielle, "Using penalized contrasts for the change-point problem", Signal Process., Vol. 85, No. 8, pp. 1501-1510, 2005.