基于光子计数激光雷达的时域去噪

骆乐; 吴长强; 林杰; 冯振超; 何伟基; 陈钱

doi:10.3788/OPE.20182605.1175

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基于光子计数激光雷达的时域去噪

信息科学 | 更新时间：2020-08-13

- 基于光子计数激光雷达的时域去噪
- Time-domain denoising based on photon-counting LiDAR
- 光学精密工程 2018年26卷第5期页码：1175-1180
- 作者机构：
  
  南京理工大学电子工程与光电技术学院, 江苏南京 210094
- 作者简介：
  
  [ "骆乐(1986-), 男, 江苏宿迁人, 博士研究生, 主要从事激光雷达三维成像、微光成像器件的研究。E-mail:wslla@126.com" ]
  何伟基(1982-), 男, 广东韶关人, 博士, 副研究员, 2004年、2010年于南京理工大学分别获得学士、博士学位, 主要从事光电探测与光电成像的研究。E-mail:hewj@mail.njust.edu.cn HE Wei-ji, E-mail:hewj@mail.njust.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61271332);中央高校基本科研业务费专项资金资助项目(30920140112012);高维信息、智能感知与系统教育部重点实验室创新基金项目(JYB201509)
- DOI：10.3788/OPE.20182605.1175
  中图分类号： TN958.98
- 收稿日期：2017-09-30，
  
  录用日期：2017-11-6，
  
  纸质出版日期：2018-05-25
- 稿件说明：
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骆乐, 吴长强, 林杰, 等. 基于光子计数激光雷达的时域去噪[J]. 光学精密工程, 2018,26(5):1175-1180.

le LUO, Chang-qiang WU, Jie LIN, et al. Time-domain denoising based on photon-counting LiDAR[J]. Optics and precision engineering, 2018, 26(5): 1175-1180.
骆乐, 吴长强, 林杰, 等. 基于光子计数激光雷达的时域去噪[J]. 光学精密工程, 2018,26(5):1175-1180. DOI： 10.3788/OPE.20182605.1175.

le LUO, Chang-qiang WU, Jie LIN, et al. Time-domain denoising based on photon-counting LiDAR[J]. Optics and precision engineering, 2018, 26(5): 1175-1180. DOI： 10.3788/OPE.20182605.1175.

摘要

激光雷达传统的成像方法需要经过长时间积分探测生成光子计数统计直方图的方式来减少背景噪声的影响，获得目标场景的深度估计信息。为了快速准确地获取目标场景的3D图像，提出基于光子计数激光雷达的三维距离图像时域去噪算法。该算法不需要生成光子计数统计直方图，利用信号和噪声在时间轴上不同的分布特性，结合了泊松过程统计规律。此算法提高了信号的探测概率，能够在低信噪比的环境下将信号和噪声分离，获得目标场景准确的3D图像。实验结果表明在低信噪比的条件下，此算法获得深度图像的RMSE与传统基于最大似然估计成像方法相比成像精度至少提高了3倍。有利于激光雷达三维成像在高背景噪声环境下的使用，拓宽了激光雷达的应用范围。

Abstract

For decreasing the effect of background noise

the traditional imaging method of laser radar requires take long time in accumulation sampling and generating statistical histogram of photon countingto obtain the depth estimation of target. A 3D time-domain denoising algorithm based on photon-counting laser radar was proposed in this paper. Combined with the Poisson statistical model

the method proposed did not need to generate photon counting statistic histogram but used the different distribution feature of signal and noise in the time-domain

which increased the detection probability of signal photons and separated the signal from the noise to recover an accurate depth image of scene in the environment of low signal-to-noise rate. Experimental results demonstrate that the method increases the imaging accuracy by 3-fold at least comparing to the traditional maximum likelihood depth estimation. The method is conducive to the use of laser radar 3D imaging in high background noise environment and could broaden the application range of Lidar.

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Keywords

references

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