Time-domain denoising based on photon-counting LiDAR

Le, 骆乐 Luo; Chang-qiang, 吴长强 Wu; Jie, 林杰 Lin; Zhen-chao, 冯振超 Feng; Weiji, 何伟基 He; Chen, Qian

doi:10.3788/ope.20182605.1175

Cited by 1 publication

(9 citation statements)

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“…In contrast, the target RD of the algorithm reported in Ref. 18 and the proposed algorithm is >60%, and the target RD of the proposed algorithm is the highest among the considered algorithms for all statistical frame numbers. When the number of imaging frames is 60, the target RD reaches 88.82%.…”

Section: Simulation and Experimentsmentioning

confidence: 71%

“…To verify the performance of the proposed algorithm for target depth estimation, it is compared with state-of-the-art approaches 16 – 19 in simulation experiments involving fixed imaging frame numbers with different SBRs and fixed SBRs with different imaging frame numbers. We use the target reduction degree (RD) between the simulated reconstructed depth image and real image to evaluate the effect of the filter, and the variance is considered to evaluate the stability of the algorithm.…”

Section: Simulation and Experimentsmentioning

confidence: 99%

“…As in the experiment described in Sec. 4.1.2, the number of statistical frames is set as 60, and the performances of the existing depth estimation algorithms 16 – 19 and proposed algorithm in reconstructing the depth images are compared when the SBR is 0.5, 0.7, 0.9, and 1. Figure 8 shows the corresponding results.…”

Section: Simulation and Experimentsmentioning

confidence: 99%

“…The algorithms reported in Refs. 18 and 19 correctly restore a part of the pixel distance value of the aircraft model in the range image. However, they cannot extract the target depth information as the SBR decreases, resulting in a large amount of noise in the reconstructed image.…”

Section: Simulation and Experimentsmentioning

confidence: 99%

“…proposed a time-domain denoising algorithm for a three-dimensional range image based on a photon-counting LIDAR. Instead of generating photon-counting statistical histograms, this algorithm exploits the different distribution characteristics of the signal and noise on the time axis to obtain the target depth information and accurately reconstruct the target range image 18 . Jian et al.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive fading Kalman depth estimation with chi-square hypothesis testing for Geiger-mode avalanche photo diode LIDAR

et al. 2023

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.The operation of a Geiger-mode avalanche photo diode (GM-APD) LIDAR is severely disturbed by background noise, which makes it challenging to rapidly and accurately estimate the target depth. Therefore, we propose an adaptive fading Kalman depth estimation technique based on chi-square hypothesis testing for GM-APD LIDARs. First, by analyzing the consistency of the echo photon distribution between adjacent pixels of the GM-APD, the pixels are fused to rapidly obtain the echo data of the target surface, thereby accelerating the depth estimation process. Second, we design a chi-square hypothesis test condition based on the statistical characteristics of the innovation vector, which can help evaluate whether the fading factor is introduced at the current moment, promote the convergence of the algorithm, and reduce the depth estimation time. Third, we propose a fading memory index weighted method to adaptively adjust the weight of the observed values to accurately estimate the innovation matrix covariance and determine the optimal fading factor. We demonstrate the effectiveness of the proposed algorithm through simulations and experiments. The results show that the proposed algorithm can rapidly and accurately estimate the target depth in the presence of strong background noise.

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