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LIDAR Point Cloud Augmentation for Dusty Weather Based on a Physical Simulation

Author

Listed:
  • Haojie Lian

    (Key Laboratory of In-Situ Property-Improving Mining of Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China)

  • Pengfei Sun

    (Key Laboratory of In-Situ Property-Improving Mining of Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China)

  • Zhuxuan Meng

    (Academy of Military Science, Beijing 100091, China)

  • Shengze Li

    (Academy of Military Science, Beijing 100091, China)

  • Peng Wang

    (Key Laboratory of In-Situ Property-Improving Mining of Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China)

  • Yilin Qu

    (School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
    Unmanned Vehicle Innovation Center, Ningbo Institute of NPU, Ningbo 315048, China)

Abstract

LIDAR is central to the perception systems of autonomous vehicles, but its performance is sensitive to adverse weather. An object detector trained by deep learning with the LIDAR point clouds in clear weather is not able to achieve satisfactory accuracy in adverse weather. Considering the fact that collecting LIDAR data in adverse weather like dusty storms is a formidable task, we propose a novel data augmentation framework based on physical simulation. Our model takes into account finite laser pulse width and beam divergence. The discrete dusty particles are distributed randomly in the surrounding of LIDAR sensors. The attenuation effects of scatters are represented implicitly with extinction coefficients. The coincidentally returned echoes from multiple particles are evaluated by explicitly superimposing their power reflected from each particle. Based on the above model, the position and intensity of real point clouds collected from dusty weather can be modified. Numerical experiments are provided to demonstrate the effectiveness of the method.

Suggested Citation

  • Haojie Lian & Pengfei Sun & Zhuxuan Meng & Shengze Li & Peng Wang & Yilin Qu, 2023. "LIDAR Point Cloud Augmentation for Dusty Weather Based on a Physical Simulation," Mathematics, MDPI, vol. 12(1), pages 1-15, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:141-:d:1311373
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    References listed on IDEAS

    as
    1. Muhammed Kürşad Uçar & Majid Nour & Hatem Sindi & Kemal Polat, 2020. "The Effect of Training and Testing Process on Machine Learning in Biomedical Datasets," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-17, May.
    2. Islam, Mazharul & Alharthi, Majed & Alam, Md. Mahmudul, 2018. "The Impacts of Climate Change on Road Traffic Accidents in Saudi Arabia," OSF Preprints 2p5aj, Center for Open Science.
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