We describe a scanning time-of-flight system which uses the time-correlated single-photon counting technique to produce three-dimensional depth images of distant, noncooperative surfaces when these targets are illuminated by a kHz to MHz repetition rate pulsed laser source. The data for the scene are acquired using a scanning optical system and an individual single-photon detector. Depth images have been successfully acquired with centimeter $xyz$ resolution, in daylight conditions, for low-signature targets in field trials at distances of up to $325\mathrm{m}$ using an output illumination with an average optical power of less than $50\mathrm{\mu W}$.

A depth imaging system, based on the time-of-flight approach and the time-correlated single-photon counting (TCSPC) technique, was investigated for use in highly scattering underwater environments. The system comprised a pulsed supercontinuum laser source, a monostatic scanning transceiver, with a silicon single-photon avalanche diode (SPAD) used for detection of the returned optical signal. Depth images were acquired in the laboratory at stand-off distances of up to 8 attenuation lengths, using per-pixel acquisition times in the range 0.5 to 100 ms, at average optical powers in the range 0.8 nW to 950 μW. In parallel, a LiDAR model was developed and validated using experimental data. The model can be used to estimate the performance of the system under a variety of scattering conditions and system parameters.

Time-correlated single-photon counting techniques have been applied to time-of-flight ranging and imaging. This article describes recent progress in photon-counting systems performing surface mapping using point-by-point acquisition of noncooperative targets at short ranges of the order of 1-50 m, as well as measurements on distributed targets at longer ranges of the order of a 100 m to a few kilometers. We describe the measurement approach, the signal analysis methodology and algorithm selection.

We present and discuss the case for full waveform pixel and image acquisition and processing to enable LIDAR sensors to penetrate and reconstruct 3D surface maps through obscuring media. To that end, we review work on signal propagation, on scanning and arrayed sensors, on signal processing strategies for independent pixels and employing spatial context, on reducing complexity and accelerating processing by sensor design, algorithmic changes, compressed sensing, and parallel processing. We report several experimental studies on LiDAR imaging through complex media, and how these can inform the automotive LiDAR scenario. We conclude with a discussion of future development and potential for full waveform LiDAR (FWL).

Time-correlated single photon counting and burst illumination laser data can be used for range profiling and target classification. In general, the problem is to analyze the response from a histogram of either photon counts or integrated intensities to assess the number, positions, and amplitudes of the reflected returns from object surfaces. The goal of our work is a complete characterization of the 3D surfaces viewed by the laser imaging system. The authors present a unified theory of pixel processing that is applicable to both approaches based on a Bayesian framework, which allows for careful and thorough treatment of all types of uncertainties associated with the data. We use reversible jump Markov chain Monte Carlo (RJMCMC) techniques to evaluate the posterior distribution of the parameters and to explore spaces with different dimensionality. Further, we use a delayed rejection step to allow the generated Markov chain to mix better through the use of different proposal distributions. The approach is demonstrated on simulated and real data, showing that the return parameters can be estimated to a high degree of accuracy. We also show some practical examples from both near and far-range depth imaging.