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基于三维卷积的Gm-APD单光子激光雷达时空联合去噪算法

王溶影 夏团结 丁军峰 马杰 毕晓文

飞控与探测2026,Vol.9Issue(1):41-49,9.
飞控与探测2026,Vol.9Issue(1):41-49,9.DOI:10.20249/j.cnki.2096-5974.2026.01.004

基于三维卷积的Gm-APD单光子激光雷达时空联合去噪算法

Space Time Joint Denoising Algorithm for Gm-APD Single Photon LiDAR Based on 3D Convolution

王溶影 1夏团结 2丁军峰 1马杰 1毕晓文3

作者信息

  • 1. 华中科技大学 人工智能与自动化学院·武汉·430074
  • 2. 上海航天控制技术研究所·上海·201109
  • 3. 中国人民解放军陆军工程大学军械士官学校·武汉·430000
  • 折叠

摘要

Abstract

Geiger-mode Avalanche Photodiode(Gm-APD)imaging is typically accompanied by sig-nificant noise interference,which poses challenges to the accurate extraction of target information.To address the issues of low signal-to-noise ratio(SNR)and extremely weak echo signals,this pa-per proposes a spatiotemporal joint denoising method based on 3 D convolution.The algorithm le-verages the high correlation of Gm-APD LiDAR echo signals in both the temporal and spatial do-mains,utilizing a designed 3 D convolution kernel to fully extract and integrate temporal and spatial information.The method first counts the histogram of the trigger frequency of each pixel across multiple consecutive frames in the temporal domain.Then,it employs the designed 3 D con-volution kernel to extract temporal and spatial information,obtaining an optimized trigger histo-gram,and distinguishing target points from noise points through threshold segmentation.Experi-ments are conducted on actual Gm-APD LiDAR datasets.The experimental results demonstrate that,compared to the peak-threshold method,the proposed method achieves a 39.96%improve-ment in target recovery and a 9.15-fold increase in SNR under the condition of 200-frame fusion denoising,indicating enhanced denoising performance.

关键词

单光子激光雷达/盖革式雪崩光电二极管/噪声抑制/三维卷积/激光雷达

Key words

single-photon LiDAR/Gm-APD/noise suppression/3 D convolution/LiDAR

分类

信息技术与安全科学

引用本文复制引用

王溶影,夏团结,丁军峰,马杰,毕晓文..基于三维卷积的Gm-APD单光子激光雷达时空联合去噪算法[J].飞控与探测,2026,9(1):41-49,9.

基金项目

湖北省科技厅自然科学基金(2024AFB1012) (2024AFB1012)

飞控与探测

2096-5974

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