• Laser & Optoelectronics Progress
  • Vol. 62, Issue 8, 0828004 (2025)
Zeyu Guo1,2,3,4, Zhen Chen1,2,3,*, Bo Liu1,2,3,4, Enhai Liu1,2,3, and Huachuang Wang1,2,3
Author Affiliations
  • 1National Laboratory on Adaptive Optics, Chengdu 610209, Sichuan , China
  • 2Key Laboratory of Science and Technology on Space Optoelectronic Precision Measurement, Chengdu 610209, Sichuan , China
  • 3Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, Sichuan , China
  • 4University of Chinese Academy of Sciences, Beijing 100049, China
  • show less
    DOI: 10.3788/LOP241859 Cite this Article Set citation alerts
    Zeyu Guo, Zhen Chen, Bo Liu, Enhai Liu, Huachuang Wang. Research on Single-Photon Sparse Point Cloud Spatio-Temporal Correlation Filtering Algorithm[J]. Laser & Optoelectronics Progress, 2025, 62(8): 0828004 Copy Citation Text show less
    Principle of Hough transform. (a) Simulated echo signal points to be detected; (b) echo signal points are converted to sine curves in polar coordinate by the Hough transform; (c) detection of a straight line connecting the echo signal points
    Fig. 1. Principle of Hough transform. (a) Simulated echo signal points to be detected; (b) echo signal points are converted to sine curves in polar coordinate by the Hough transform; (c) detection of a straight line connecting the echo signal points
    Schematic diagram of the definition for the parameters of Hough transform
    Fig. 2. Schematic diagram of the definition for the parameters of Hough transform
    Flowchart of proposed algorithm
    Fig. 3. Flowchart of proposed algorithm
    Sine curves of different resolutions. (a) Resolution of 0.1; (b) resolution of 0.5; (c) resolution of 1.5; (d) resolution of 5.0
    Fig. 4. Sine curves of different resolutions. (a) Resolution of 0.1; (b) resolution of 0.5; (c) resolution of 1.5; (d) resolution of 5.0
    Simulated point cloud under extreme motion condition. (a) Point cloud; (b) localization image of point cloud
    Fig. 5. Simulated point cloud under extreme motion condition. (a) Point cloud; (b) localization image of point cloud
    Processed results of simulated data by proposed algorithm. (a) Overall result; (b) result of large error area (region ①); (c) result of low signal-to-noise ratio area (region ②)
    Fig. 6. Processed results of simulated data by proposed algorithm. (a) Overall result; (b) result of large error area (region ①); (c) result of low signal-to-noise ratio area (region ②)
    Schematic diagram of experimental facilities
    Fig. 7. Schematic diagram of experimental facilities
    Schematic diagram of experimental process
    Fig. 8. Schematic diagram of experimental process
    Processed results of measured echo point cloud. (a) Overall map of measured echo point cloud; (b) point cloud of local high signal-to-noise ratio area (region ①); (c) point cloud of local sparse area (region ②)
    Fig. 9. Processed results of measured echo point cloud. (a) Overall map of measured echo point cloud; (b) point cloud of local high signal-to-noise ratio area (region ①); (c) point cloud of local sparse area (region ②)
    Comparison between traditional Hough transform and the proposed algorithm. (a) Traditional Hough transform; (b) proposed algorithm
    Fig. 10. Comparison between traditional Hough transform and the proposed algorithm. (a) Traditional Hough transform; (b) proposed algorithm
    Processed results of measured data by proposed algorithm. (a) Measured echo point cloud; (b) part of the point cloud that needs to be processed; (c) extracted effective echo point cloud; (d) overall data processed result; (e) processed result of high signal-to-noise ratio area (region ①); (f) processed result of partial echo missing area (region ②)
    Fig. 11. Processed results of measured data by proposed algorithm. (a) Measured echo point cloud; (b) part of the point cloud that needs to be processed; (c) extracted effective echo point cloud; (d) overall data processed result; (e) processed result of high signal-to-noise ratio area (region ①); (f) processed result of partial echo missing area (region ②)
    Processed results of measured data by proposed algorithm. (a) Measured echo point cloud; (b) part of the point cloud that needs to be processed; (c) extracted effective echo point cloud; (d) overall data processed result; (e) predicted trajectory corrected result after the effective echo is extracted (region ①); (f) Kalman filter prediction result when the echo is missing (region ②)
    Fig. 12. Processed results of measured data by proposed algorithm. (a) Measured echo point cloud; (b) part of the point cloud that needs to be processed; (c) extracted effective echo point cloud; (d) overall data processed result; (e) predicted trajectory corrected result after the effective echo is extracted (region ①); (f) Kalman filter prediction result when the echo is missing (region ②)
    ParameterValue
    Single pulse energy of the laser Et /μJ18.75
    Pulse repetition frequency (PRF) /kHz8
    Laser wavelength λ /nm1064
    Laser divergence angle θt /mrad1.13
    Bandwidth of narrow band filter (FWHM) /nm1
    Quantum efficiency of Gm-APD ηqe /%2.8
    Dead time of Gm-APD td /ns41
    Table 1. Main parameters of single-photon LiDAR tracking and ranging system
    Zeyu Guo, Zhen Chen, Bo Liu, Enhai Liu, Huachuang Wang. Research on Single-Photon Sparse Point Cloud Spatio-Temporal Correlation Filtering Algorithm[J]. Laser & Optoelectronics Progress, 2025, 62(8): 0828004
    Download Citation