A combined denoising algorithm for roadside LiDAR point clouds under snowy condition

Quanli Lu; Xiaowei Lan; Jiabin Xu; Lihua Song; Bin Lv; Jianqing Wu

doi:10.1117/12.2627982

14 March 2022 A combined denoising algorithm for roadside LiDAR point clouds under snowy condition

Quanli Lu, Xiaowei Lan, Jiabin Xu, Lihua Song, Bin Lv, Jianqing Wu

Proceedings Volume 12165, International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021); 121651B (2022) https://doi.org/10.1117/12.2627982
Event: 2021 International Conference on Intelligent Traffic Systems and Smart City, 2021, Zhengzhou, China

Abstract

Roadside Light Detection and Ranging (LiDAR) can provide over the horizon perception information for connected vehicles (CV). However, its performance may be affected by the weather, especially in rainy and snowfall weather. To improve all-weather working ability, a combined denoising algorithm is proposed in this paper after analyzing the shortcomings of the existing filters. The filter is composed of crop box filter, ray ground filter, voxel filter, and statistical outlier filter. By combining multiple point clouds filters, the snowfall points are removed and the effectiveness of general filters in complex scenes is verified. The experiment shows that it not only can retain the traffic objects’ features, but also realize denoising on real point clouds data of snowfall weather.

Citation Download Citation

Quanli Lu, Xiaowei Lan, Jiabin Xu, Lihua Song, Bin Lv, and Jianqing Wu "A combined denoising algorithm for roadside LiDAR point clouds under snowy condition", Proc. SPIE 12165, International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021), 121651B (14 March 2022); https://doi.org/10.1117/12.2627982

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