Andrew M. Wallace

Long-range time-of-flight scanning sensor based on high-speed time-correlated single-photon counting

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 m using an output illumination with an average optical power of less than 50 microW.

Laser-based distance measurement using picosecond resolution time-correlated single-photon counting

In this paper, we report results obtained with a time-of-flight ranging/scanning system based on time-correlated single-photon counting. This system uses a pulsed picosecond diode laser and detects the scattered signal from a non-cooperative target surface using a semiconductor single-photon detector. A demonstration system has been constructed and used to examine the depth resolution obtainable as a function of the integrated number of photon returns. The depth resolution has been examined for integrated photon returns varying by five orders of magnitude, both by obtaining experimental measurements and by computer simulation. Depth resolutions of approximately 3 mm were obtained for only ten returned photons. The effect of the background signal, originating either from temporally uncorrelated light signals or from detector noise, has also been examined.

Model-based planning of optimal sensor placements for inspection

We report a system for sensor planning, GASP, which is used to compute the optimal positions for inspection tasks using known imaging sensors and feature-based object models. GASP (general automatic sensor planning) uses a feature inspection representation (the FIR), which contains the explicit solution for the simplest sensor positioning problem. The FIR is generated off-line, and is exploited by GASP to compute on-line plans for more complex tasks, called inspection scripts. Viewpoint optimality is defined as a function of feature visibility and measurement reliability. Visibility is computed using an approximate model. Reliability of inspection depends on both the physical sensors acquiring the images and on the processing software; therefore we include both these components in a generalized sensor model. These predictions are based on experimental, quantitative assessment. We show how these are computed for a real generalized sensor, which includes a 3-D range imaging system, and software performing robust outlier removal, surface segmentation, object location and surface fitting. Finally, we demonstrate a complete inspection session involving 3-D object positioning, planning optimal position inspection, and feature measurement from the optimal viewpoint.

Ranging and Three-Dimensional Imaging Using Time-Correlated Single-Photon Counting and Point-by-Point Acquisition

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.

Bayesian Analysis of Lidar Signals with Multiple Returns

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.

Subcentimeter depth resolution using a single-photon counting time-of-flight laser ranging system at 1550 nm wavelength

We demonstrate subcentimeter depth profiling at a stand off distance of 330 m using a time-of-flight approach based on time-correlated single-photon counting. For the first time to our knowledge, the photon-counting time-of-flight technique was demonstrated at a wavelength of 1550 nm using a superconducting nanowire single-photon detector. The performance achieved suggests that a system using superconducting detectors has the potential for low-light-level and eye-safe operation. The systems instrumental response was 70 ps full width at half-maximum, which meant that 1 cm surface-to-surface resolution could be achieved by locating the centroids of each return signal. A depth resolution of 4 mm was achieved by employing an optimized signal-processing algorithm based on a reversible jump Markov chain Monte Carlo method.

Time-of-flight optical ranging system based on time-correlated single-photon counting

The design and implementation of a prototype time-of-flight optical ranging system based on the time-correlated single-photon-counting technique are described. The sensor is characterized in terms of its longitudinal and transverse spatial resolution, single-point measurement time, and long-term stability. The system has been operated at stand-off distances of 0.5-5 m, has a depth repeatability of <30 mum, and has a lateral spatial resolution of <500 mum.

Laser depth measurement based on time-correlated single-photon counting

A method for acquiring range data based on time-correlated single-photon counting is described. This method uses a short-pulse ( approximately 10-ps) laser diode, a detector based on a silicon single-photon avalanche diode, and standard photon-counting timing electronics. The accuracy of the technique has been measured as approximately +/-30 microm in a laboratory experiment and corresponds closely to the results of a theoretical simulation.

Optical design and evaluation of a three-dimensional imaging and ranging system based on time-correlated single-photon counting

The design and operation of a noncontact surface profilometry system based on the time-correlated single-photon-counting technique are described. This system has a robust optomechanical design and uses an eye-safe laser that makes it particularly suitable for operation in an uncontrolled industrial environment. The sensitivity of the photon-counting technique permits its use on a variety of target materials, and its mode of operation does not require the continual presence of an operator. The system described has been optimized for a 1-25-m standoff, has a distance repeatability of <30 microm, and has a transverse spatial resolution of approximately 60 microm at a 2-m standoff and approximately 400 microm at a 13-m standoff.

Recovery of Forest Canopy Parameters by Inversion of Multispectral LiDAR Data

We describe the use of Bayesian inference techniques, notably Markov chain Monte Carlo (MCMC) and reversible jump MCMC (RJMCMC) methods, to recover forest structural and biochemical parameters from multispectral LiDAR (Light Detection and Ranging) data. We use a variable dimension, multi-layered model to represent a forest canopy or tree, and discuss the recovery of structure and depth profiles that relate to photochemical properties. We first demonstrate how simple vegetation indices such as the Normalized Differential Vegetation Index (NDVI), which relates to canopy biomass and light absorption, and Photochemical Reflectance Index (PRI) which is a measure of vegetation light use efficiency, can be measured from multispectral data. We further describe and demonstrate our layered approach on single wavelength real data, and on simulated multispectral data derived from real, rather than simulated, data sets. This evaluation shows successful recovery of a subset of parameters, as the complete recovery problem is ill-posed with the available data. We conclude that the approach has promise, and suggest future developments to address the current difficulties in parameter inversion.