Infrared and Laser Engineering, Volume. 52, Issue 5, 20220682(2023)
Adaptive spatial-temporal correlation depth estimation of photon-counting lidar
Fig. 2. (a) Overall flow chart; (b) Adaptive selection of the time window
Fig. 3. Neighborhood data set (3×3). (a) The time interval of the maximum counts of each pixel in the neighborhood; (b) The time interval of the neighborhood data set and the corresponding photon counts in the original histogram
Fig. 4. (a) The original terrain; (b) Photon statistical histogram (
Fig. 5. Comparison of reconstruction results of different algorithms at different noise intensities. (a) 0.2 MHz noise, 14.3 PPP; (b) 2 MHz noise, 13.8 PPP; (c) 3.5 MHz noise, 14.6 PPP; (d) 6 MHz noise, 14.1 PPP
Fig. 6. (a) Block diagram of photon counting lidar system; (b) Physical picture of device
Fig. 7. (a) Visible band image; (b) Reconstructed truth depth image of target
Fig. 8. Reconstruction results of target. (a) 0.23 MHz noise, 21.6 PPP; (b) 1.21 MHz noise, 21.2 PPP; (c) 2.17 MHz noise, 21.8 PPP; (d) 3.02 MHz noise, 22.5 PPP; (e) 5.08 MHz noise, 23.9 PPP
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Rui Wang, Bo Liu, Zhikang Li, Zhen Chen, Hao Yi. Adaptive spatial-temporal correlation depth estimation of photon-counting lidar[J]. Infrared and Laser Engineering, 2023, 52(5): 20220682
Category: Laser & laser optics
Received: Sep. 21, 2022
Accepted: --
Published Online: Jul. 4, 2023
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