Research on single photon point cloud denoising algorithm for airborne targets based on feature point matching

Wanshun Sun; Nanxiang Zhao; Yihua Hu; Shuo Wei

doi:10.1117/12.2686756

6 August 2023 Research on single photon point cloud denoising algorithm for airborne targets based on feature point matching

Wanshun Sun, Nanxiang Zhao, Yihua Hu, Shuo Wei

Author Affiliations +

Proceedings Volume 12781, International Conference on Optoelectronic Information and Functional Materials (OIFM 2023); 127811A (2023) https://doi.org/10.1117/12.2686756
Event: 2023 International Conference on Optoelectronic Information and Functional Materials (OIFM 2023), 2023, Guangzhou, JS, China

Abstract

When single photon counting lidar detects moving targets in the air, the traditional denoising algorithm is not effective because of the short photon accumulation time and more susceptibility to noise photons. This paper first analyzes the spatial distribution characteristics of the point cloud of the air target, denoises the single frame point cloud through radius filtering and grid filtering, and then calculates the fitting lines and feature points of the air target by using the line fitting algorithm and density clustering algorithm and corrects the position of the feature points by calculating the spatial distribution characteristics of the point cloud. Multi-frame point clouds are fused by the method of feature point matching and de-noised according to the detection probability. Compared with the traditional filtering de-noising algorithm, the proportion of noise point clouds in the de-noising result is 0.06, and the Hausdorff distance is 1.29. The de-noising effect is obvious, and the contour information of the target is well preserved. The results show that the method of feature point matching is feasible for point cloud denoising moving objects.

Citation Download Citation

Wanshun Sun, Nanxiang Zhao, Yihua Hu, and Shuo Wei "Research on single photon point cloud denoising algorithm for airborne targets based on feature point matching", Proc. SPIE 12781, International Conference on Optoelectronic Information and Functional Materials (OIFM 2023), 127811A (6 August 2023); https://doi.org/10.1117/12.2686756

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KEYWORDS

Point clouds

Detection and tracking algorithms

Tunable filters

Target detection

Denoising

Signal detection

Single photon detectors

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