2018
DOI: 10.1117/1.oe.57.3.031306
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Peak detection approaches for time-correlated single-photon counting three-dimensional lidar systems

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Cited by 30 publications
(11 citation statements)
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“…Application of Equation ( 1) smooths the histogram sequence. By searching for maximum value locations in the smoothed histogram, the depth information for a single beam can be extracted [30]. For some beams that do not irradiate onto the target or for which the signal-to-noise ratio is extremely poor, mis-extraction can also happen, but the rarely misextracted depth points in the 3D image can be quickly removed by voxel filtering [31,32].…”
Section: Data Processing Methodsmentioning
confidence: 99%
“…Application of Equation ( 1) smooths the histogram sequence. By searching for maximum value locations in the smoothed histogram, the depth information for a single beam can be extracted [30]. For some beams that do not irradiate onto the target or for which the signal-to-noise ratio is extremely poor, mis-extraction can also happen, but the rarely misextracted depth points in the 3D image can be quickly removed by voxel filtering [31,32].…”
Section: Data Processing Methodsmentioning
confidence: 99%
“…with sub-bin precision) and the range walk error resulting from the distortion to be compensated for [28]. Techniques for peak extraction include iterative curve fitting [29], as well as filtering the LIDAR waveform (histogram) using a finite impulse response filter (FIR) matching the temporal profile of the anticipated signal peak [30]. It has been shown that even the computationally modest approach of local centroiding of the histogram (following background compensation) can result in a performance approaching the Cramér-Rao bounds that define the lowest possible variance for an unbiased estimator (see, e.g.…”
Section: B Histogram Memory and Peak Identificationmentioning
confidence: 99%
“…The bin width δ corresponds to the timing resolution of the system, and the photon count in each bin is subject to Poisson noise. A potential way to obtain the depth estimate, as encoded in the time position of the histogram peak, is via iterative curve fitting [28]. However, a simple centre-of-mass method (CMM) [29], as adopted here, leads to similar performance, while enabling real-time processing and depth visualization for an entire ToF sensor array.…”
Section: Theory (A) Inferring Distance From Time-of-flight Measurementsmentioning
confidence: 99%