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

Haojie Lian, Pengfei Sun, Zhuxuan Meng, Shengze Li, Peng Wang and Yilin Qu ()
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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

Mathematics, 2023, vol. 12, issue 1, 1-15

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.

Keywords: LIDAR; 3D point cloud; physics simulation; adverse weather; object detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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