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Conference Paper RGOR: De-noising of LiDAR point clouds with reflectance restoration in adverse weather
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Authors
Seung-Jun Han, Dongjin Lee, Kyoung-Wook Min, Jeongdan Choi
Issue Date
2023-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2023, pp.1844-1849
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC58733.2023.10392388
Abstract
Recently, LiDAR sensors have become indispensable in autonomous driving research. Despite continuous improvements in performance and price reductions, noise generated under adverse weather conditions remains a serious challenge. Most of the noise generated under such conditions is due to particles such as fog, rain, and snow. These particles are extremely fine; therefore, they have a very low reflectance compared to the targets that the laser should detect. In this study, we propose a method to distinguish particles by restoring the reflectance from LiDAR sensing data based on the reflectance characteristics of the particles. In addition, we propose a method to make additional judgments based on the geometrical shapes of adjacent particles to distinguish the particles more accurately. The proposed method is accurate enough to be compared to state-of-the-art deep learning methods. Moreover, the execution time is less than 2 ms on a single-core CPU, demonstrating a remarkable efficiency, being more than three times faster than that of methods performed on a GPU. Because noise removal is a preprocessing step, the proposed method is expected to allow more resources to be allocated to other, more important processes for autonomous driving.
KSP Keywords
Adverse Weather Conditions, Continuous Improvements, De-noising, Learning methods, LiDAR sensors, Noise Removal, Point clouds, Sensing data, autonomous driving, deep learning(DL), execution time