Photon Lidar Research Articles

Surface roughness metrology with a raster scanning single photon LiDAR

We explore a novel, to the best of our knowledge, approach to surface roughness metrology utilizing a single pixel, raster scanning single photon counting LiDAR system. It uses a collimated laser beam in picosecond pulses to probe a surface, capturing the changes of back-scattered photons from different points on the surface into a single mode fiber, and counting them using a single photon detector. These back-scattered photons carry speckle noise produced by the rough surface, and the variation in photon counts over different illumination points across the surface becomes a good measure of its roughness. By analyzing the variation frequency as the LiDAR scans over the surface using machine learning techniques, we demonstrate general measurements of surface roughness from 1.21 (1.27±4.51) to 102.01 (87.97±10.55) microns.

Marine remote target signal extraction based on 128 line-array single photon LiDAR

LiDAR technology has garnered significant attention in recent years due to its superior directivity, high resolution, and precise 3D information acquisition capabilities, making it indispensable in navigation systems. Among its variants, single-photon LiDAR stands out for maritime applications, owing to its reduced power consumption and extended detection range. However, the high sensitivity of single-photon detectors often results in substantial noise, necessitating effective denoising before data can be utilized for identification, tracking, and other purposes. In this study, we present a novel 128-line, 1550 nm shipborne long-range single-photon LiDAR system, with data collected and analyzed in maritime environments. This system contends with challenges such as a large dynamic range, abundant noise photons, and the complexity of sea surface conditions. To address these issues, we propose an efficient and adaptive denoising algorithm based on the k-th nearest neighbor (KNN) methodology. By examining the distribution characteristics of signal and noise photons, our approach enables target extraction even under conditions of intense noise and sparse signals. Our method exhibits robust adaptability across various detection scenarios. Experimental evaluations demonstrate its efficacy, accurately identifying targets at distances of 3.2 km in clear weather and 1.6 km in foggy conditions.

Enhancing angular resolution in single-photon LIDAR for daytime long-range 3D imaging

Single photon LIDAR exhibits exceptional detection sensitivity and finds extensive applications in long-distance measurement and three-dimensional imaging domains. However, it is susceptible to background noise interference, leading to a trade-off between scanning imaging efficiency and angular resolution, thereby limiting its overall performance. In this paper, we propose a novel ‘compression-resampling’ high-resolution imaging method for single-photon scanning LIDAR based on the spatial correlation of the target scene. This approach significantly enhances the system's angular resolution without compromising imaging efficiency. By employing this method, we successfully conducted 3D imaging over distances exceeding 26 km even under challenging conditions with high levels of background noise during daylight hours. Within a target field of view measuring 1° × 1°, we reconstructed a high-resolution 3D point cloud image comprising 1000 × 1410 pixels. Notably, this achievement yielded an impressive angular resolution of 17.4 urad while maintaining an acceptable imaging time of approximately 20 minutes. The results unequivocally demonstrate the effectiveness of our proposed method in improving the quality of three-dimensional imaging and enhancing angular resolution while simultaneously reducing imaging time requirements, thus expanding potential application scenarios for single-photon LIDAR technology.

High accuracy ranging for space debris with spaceborne single photon Lidar.

The increasing risk posed by space debris highlights the need for accurate localization techniques. Spaceborne single photon Lidar (SSPL) offers a promising solution, overcoming the limitations of traditional ground-based systems by providing expansive coverage and superior maneuverability without being hindered by weather, time, or geographic constraints. This study introduces a novel approach leveraging non-parametric Bayesian inference and the Dirichlet process mixture model (DPMM) to accurately determine the distance of space debris in low Earth orbit (LEO), where debris exhibits nonlinear, high dynamic motion characteristics. By integrating extended Kalman filtering (EKF) for range gating, our method captures the temporal distribution of reflected photons, employing Markov chain Monte Carlo (MCMC) for iterative solutions. Experimental outcomes demonstrate our method's superior accuracy over conventional statistical techniques, establishing a clear correlation between radial absolute velocity and ranging error, thus significantly enhancing monostatic space debris localization.

Artifact Removal in Long-Range Single Photon LiDAR via Waveforms-Guided Depth Image Estimation

Artifact Removal in Long-Range Single Photon LiDAR via Waveforms-Guided Depth Image Estimation

PE-RASP: range image stitching of photon-efficient imaging through reconstruction, alignment, stitching integration network based on intensity image priors.

Single photon imaging integrates advanced single photon detection technology with Laser Radar (LiDAR) technology, offering heightened sensitivity and precise time measurement. This approach finds extensive applications in biological imaging, remote sensing, and non-visual field imaging. Nevertheless, current single photon LiDAR systems encounter challenges such as low spatial resolution and a limited field of view in their intensity and range images due to constraints in the imaging detector hardware. To overcome these challenges, this study introduces a novel deep learning image stitching algorithm tailored for single photon imaging. Leveraging the robust feature extraction capabilities of neural networks and the richer feature information present in intensity images, the algorithm stitches range images based on intensity image priors. This innovative approach significantly enhances the spatial resolution and imaging range of single photon LiDAR systems. Simulation and experimental results demonstrate the effectiveness of the proposed method in generating high-quality stitched single-photon intensity images, and the range images exhibit comparable high quality when stitched with prior information from the intensity images.

Updating forest road networks using single photon LiDAR in northern Forest environments

Abstract Knowledge about the condition and location of forest roads is important for forest management. Coupling accurate forest road information with planning and conservation strategies supports forest resource management. In Canada, spatial data of forestry road networks are available provincially; however, they lack spatial accuracy, and up-to-date information on key attributes such as road width is missing. In this study, we apply a novel approach to update forest road networks and characterize road conditions in Ontario’s Boreal and Great Lakes—St. Lawrence (GLSL) Forest regions. We use airborne laser scanning (ALS), to facilitate the identification of forest roads across densely forested landscapes. We categorized roads into four classes based on driveable width, edge vegetation, as well as surface and edge degradation as derived from high-density Single Photon LiDAR (SPL) data. Using a novel road extraction method, we produced a road probability raster and map road centerlines. We validated road location and attribute information using Global Navigation Satellite System (GNSS) ground truth data in two Ontario forest management units, in the boreal forest and the GLSL. Road segments in some regions have been altered to account for land cover changes, such as flooding or fallen trees. In other situations, the road path may deviate from the planned layout of the road, which is not always followed in the field. Our results highlight inaccuracies in the existing road networks, with 30 per cent of ‘Full access’ roads and 29 per cent of ‘Partial access’ roads being undriveable by standard vehicles and 45 per cent of ‘Status unknown’ roads, which make up 48 per cent of the pre-existing network, being driveable by standard vehicles. Results show that the average positional accuracy of updated road centerlines is 0.4 m, and the average road width error is 2 m. The production of spatially accurate forest road networks and road attribute information is important for characterizing large road networks for which often minimal information is available.

Assessment of Oak Groves Conservation Statuses in Natura 2000 Sacs with Single Photon Lidar and Sentinel-2 Data

Among the main objectives of Natura 2000 Network sites management plans is monitoring their conservation status under a reasonable cost and with high temporal frequency. The aim of this study is to assess the ability of single-photon light detection and ranging (LiDAR) technology (14 points per m2) and Sentinel-2 data to classify the conservation status of oak forests in four special areas of conservation in Navarra Province (Spain) that comprise three habitats. To capture the variability of conservation status within the three habitats, we first performed a random stratified sampling based on conservation status measured in the field, canopy cover, and terrain slope and height. Thereafter, we compared two metric selection approaches, namely Kruskal–Wallis and Dunn tests, and two machine learning classification methods, random forest (RF) and support vector machine (SVM), to classify the conservation statuses using LiDAR and Sentinel-2 data. The best-fit classification model, which included only LiDAR metrics, was obtained using the random forest method, with an overall classification accuracy after validation of 83.01%, 75.51%, and 88.25% for Quercus robur (9160), Quercus pyrenaica (9230), and Quercus faginea (9240) habitats, respectively. The models include three to six LiDAR metrics, with the structural diversity indices (LiDAR height evenness index, LHEI, and LiDAR height diversity index, LHDI) and canopy cover (FCC) being the most relevant ones. The inclusion of the NDVI index from the Sentinel-2 image did not improve the classification accuracy significantly. This approach demonstrates its value for classifying and subsequently mapping conservation statuses in oak groves and other Natura 2000 Network habitat sites at a regional scale, which could serve for more effective monitoring and management of high biodiversity habitats.

Technical progress and system evaluation of all-time single photon lidar

Technical progress and system evaluation of all-time single photon lidar

Chaos single photon LIDAR and the ranging performance analysis based on Monte Carlo simulation.

With the advent of serial production lidars, single photon lidar faces an increasingly severe threat of crosstalk. In this paper, we first propose the concept of Chaos Single Photon (CSP) lidar and establish the theoretical model. In CSP lidar system, chaos laser replaces pulsed laser, and the physical random sequence generated by a Geiger mode avalanche photodiode (GM-APD) responding to chaos laser substitutes the traditional pseudo-random sequence. The mean density of '1' code of the CSP lidar system can exceed 10 million counts per second (Mcps) with a dead time immunity. The theoretical models of detection probability and false alarm rate are derived and demonstrated based on Poisson distribution. The bit error rate (BER) is introduced into the CSP lidar system for evaluating the range walk error intuitively. Additionally, the simulation results indicate that the CSP lidar system has a robust anti-crosstalk capability. Compared with the traditional pseudo-random single photon (PRSP) lidar system, the CSP lidar system not only overcomes range ambiguity but also has a signal to noise rate (SNR) of 60 times, reaching 10000 when the mean echo photoelectron number is 10 per nanosecond. Benefited from large-scale arrays and extremely high sensitivity properties of GM-APDs, we are looking forward to the applications of the CSP lidar in weak signal detection, remote mapping, autonomous driving, etc.

Study on the Parameters of Ice Clouds Based on 1.5 µm Micropulse Polarization Lidar

Dust aerosols can participate in the heterogeneous nucleation process as effective ice nucleation particles, thus changing the physical properties of clouds. In this paper, we used an eye-safe 1550 nm micropulse polarization single photon lidar combined with meteorological stations, HYSPLIT backward trajectory analysis, ERA5 reanalysis data, CALIPSO, Himawari-8 and Terra-MODIS satellite data to compare the difference in cloud characteristics between dust and clean cirrus cases in Jinan from 26–29 March 2022. The study found that the aerosol affected the cloud effective radius, and the cloud top temperature impacted the properties of depolarization of dust ice clouds. According to the statistical results of the upper and lower quartiles, the depolarization ratio (DPR) range of dust cirrus on 26 March was 0.46–0.49, a similar range to the clean cirrus, while that of dust cirrus on 27 March was 0.54–0.59, which seemed much larger. Different height and temperature conditions lead to differences in the habits of ice crystals in clouds, thus changing the DPR. However, the range of the DPR between clean cirrus and dust cirrus showed no obvious difference, as the former was 0.43–0.53 and the latter was 0.46–0.59. Under the condition of higher aerosol loading, the lidar range-corrected signal (RCS) of cirrus clouds was stronger, and the cloud effective radius was 48 μm, larger than that of clean cirrus (32 μm). This may be the effect of dust on the microphysical properties of clouds. This study discusses the indirect effects of dust aerosols on cirrus clouds and the underlying mechanisms from the perspectives of microphysics and optics, which can provide more references for urban air pollution processes and aerosol-cloud interactions.

Feasibility of Hyperspectral Single Photon Lidar for Robust Autonomous Vehicle Perception.

Autonomous vehicle perception systems typically rely on single-wavelength lidar sensors to obtain three-dimensional information about the road environment. In contrast to cameras, lidars are unaffected by challenging illumination conditions, such as low light during night-time and various bidirectional effects changing the return reflectance. However, as many commercial lidars operate on a monochromatic basis, the ability to distinguish objects based on material spectral properties is limited. In this work, we describe the prototype hardware for a hyperspectral single photon lidar and demonstrate the feasibility of its use in an autonomous-driving-related object classification task. We also introduce a simple statistical model for estimating the reflectance measurement accuracy of single photon sensitive lidar devices. The single photon receiver frame was used to receive 30 12.3 nm spectral channels in the spectral band 1200–1570 nm, with a maximum channel-wise intensity of 32 photons. A varying number of frames were used to accumulate the signal photon count. Multiple objects covering 10 different categories of road environment, such as car, dry asphalt, gravel road, snowy asphalt, wet asphalt, wall, granite, grass, moss, and spruce tree, were included in the experiments. We test the influence of the number of spectral channels and the number of frames on the classification accuracy with random forest classifier and find that the spectral information increases the classification accuracy in the high-photon flux regime from 50% to 94% with 2 channels and 30 channels, respectively. In the low-photon flux regime, the classification accuracy increases from 30% to 38% with 2 channels and 6 channels, respectively. Additionally, we visualize the data with the t-SNE algorithm and show that the photon shot noise in the single photon sensitive hyperspectral data contributes the most to the separability of material specific spectral signatures. The results of this study provide support for the use of hyperspectral single photon lidar data on more advanced object detection and classification methods, and motivates the development of advanced single photon sensitive hyperspectral lidar devices for use in autonomous vehicles and in robotics.

Viewpoint: Compact cryogenics for superconducting photon detectors

Abstract Superconducting detectors capable of registering single light quanta - single photons - are highly sought after in a host of emerging 21st century technologies [1]. Superconducting nanowire single-photon detectors (SNSPDs or SSPDs) [2] operating in the telecommunication wavelength range outperform conventional semiconductor detectors in the following metrics: dark count rate, system detection efficiency (SDE), photon counting rate and timing jitter [3, 4, 5, 6]. SNSPDs have been deployed in many important emerging photon-counting applications including single photon LiDAR [7, 8], quantum key distribution [9, 10], optical quantum computing [11], life sciences [12, 13], fibre sensing [14, 15], exoplanet spectroscopy [16] and free space optical communication [17].

Single photon imaging based on a photon driven sparse sampling.

Single photon three-dimensional (3D) imager can capture 3D profile details and see through obscuring objects with high sensitivity, making it promising in sensing and imaging applications. The key capabilities of such 3D imager lie on its depth resolution and multi-return discrimination. For conventional pulsed single photon lidar, these capabilities are limited by transmitter bandwidth and receiver bandwidth simultaneously. A single photon imager is proposed and experimentally demonstrated to implement time-resolved and multi-return imaging. Time-to-frequency conversion is performed to achieve millimetric depth resolution. Experimental results show that the depth resolution is better than 4.5 mm, even though time jitter of the SPAD reaches 1 ns and time resolution of the TCSPC module reaches 10 ns. Furthermore, photon driven sparse sampling mechanism allows us to discriminate multiple near surfaces, no longer limited by the receiver bandwidth. The simplicity of the system hardware enables low-cost and compact 3D imaging.

Noise reduction and 3D image restoration of single photon counting LiDAR using adaptive gating

Single photon LiDAR is considered as one of the most important tools in acquiring target information with high accuracy under extreme imaging conditions, as it offers single photon sensitivity and picosecond timing resolution. However, such technique sense the scene with the photons reflected by the target, thus resulting in severe degradation of image in presence of strong noise. Range gating with high-speed electronics is an effective way to suppress the noise, unfortunately, such technique suffers from manually selecting the parameters and limited gating width. This paper presents a target information extracting and image restoration method under large observation window, which first obtain the depth distribution of the target and extract the information within the range by analyzing the model of signal and noise, then further improve the image quality by adopting advanced image restoration algorithm and henceforth shows better results than those denoising method that purely relying on hardware. In the experiment, photon-per-pixel (PPP) was as low as 3.020 and signal-to-background ratio (SBR) was as low as 0.106, the proposed method is able to improve SBR with a factor of 19.330. Compared to classical algorithm named cross correlation, the reconstruction signal to noise ratio (RSNR) increased 33.520dB by further cooperating with advanced image restoration algorithm, thus improved the ability of sensing accurate target information under extreme cases.

Single - Photon Lidar for Canopy Detection with a Multi-Channel Si Spad at 1064 Nm

Single - Photon Lidar for Canopy Detection with a Multi-Channel Si Spad at 1064 Nm

PCA-based real-time single-photon 3D imaging method

The single photon lidar (SPL) system can collect data within a wide range, which is widely used in multispectral depth imaging and underwater depth imaging. However, SPL systems are subject to solar noise and dark count returns, resulting in poor quality of image construction. Therefore, a high-efficiency 3D imaging technology is needed to improve the performance of the SPL system. To address this problem, this paper develops a new short-range single-photon three-dimensional imaging method with both high quality and low complexity. In this paper, we use a 400 × 100 SPAD detector and an 850 nm laser source composed of VCSEL array to reconstruct the 3D image of a target within 5 m. By applying principal component analysis (PCA), the complexity of the proposed method can be reduced by 20 times compared with existing algorithms without compromising imaging quality, making the proposed method a promising candidate to be applied to a wide range of fields such as consumer electronics.

Single photon Lidar gas imagers for practical and widespread continuous methane monitoring

The accurate and comprehensive identification and quantification of greenhouse gas (GHG) emissions is an essential part of the management and mitigation of climate change. We are developing a novel remote gas imaging sensor for the detection, visualisation, and quantification of methane emissions. The sensor uses a new technique we call Tunable Diode Lidar (TDLidar) which combines aspects of Tunable Diode Laser Absorption Spectroscopy (TDLAS) with Differential Absorption Lidar (DIAL) and Time Correlated Single Photon Counting (TCSPC) to enable remote spectroscopy and ranging with low power semiconductor diode lasers. Our first TDLidar methane sensors use diode lasers with wavelengths around the CH4 absorption line at 1.6509 μm and Peltier-cooled Single Photon Avalanche Diode (SPAD) detectors in a Random Modulation Continuous Wave (RM-CW) Lidar system. Here we characterise our TDLidar methane sensor performance with calibrated gas cells and controlled gas release trials and we demonstrate quantification of leak rates as low as 0.012 g/s and detection at distances over 90 m. The accuracy, speed, and practicality of the sensor, combined with an expectation of low-cost in volume, offers the potential that these sensors can be effectively applied for widespread continuous and autonomous monitoring of industrial methane emissions.

Single photon lidar signal attenuation under boreal forest conditions

ABSTRACT Single-photon lidar (SPL100) is a recently commercialized airborne lidar system facilitating efficient wide-area acquisitions of high-density point clouds due to its capacity for higher altitude acquisitions compared to traditional linear-mode lidar (LML) systems. Increased acquisition efficiency and point densities make SPL100 attractive for forest management applications. SPL100 utilizes 532 nm (green wavelength) lasers, wherein there is reduced reflectance from vegetation, increased sensitivity to solar noise, and increased signal attenuation, which may impact the vertical distribution of SPL100 returns in forest canopies. We assessed SPL100 data acquisitions over managed forests in north-eastern Ontario, Canada, using high-density unmanned aerial vehicle-borne laser scanning (ULS) data as reference over a range of forest conditions with variable vertical structure. Signal attenuation depth of individual SPL100 returns was estimated through a surface model normalization approach stratified by a ULS-derived structural index that compared densities of returns in the upper canopy to low vegetation and near ground. Canopy signal attenuation was closely matched in both systems, particularly in the upper canopy and near the ground surface; however, results showed a 31% reduction in the relative characterization of mid-canopy vegetation layers by SPL100 under conditions identified by the structural index as closed canopy, compared to the ULS system.

Assessing single photon LiDAR for operational implementation of an enhanced forest inventory in diverse mixedwood forests

Airborne laser scanning (ALS; LiDAR) data are an increasingly common data source for forest inventories, and approaches integrating ALS data with field plot measurements have become operational in several jurisdictions. As technology continues to evolve, different LiDAR sensors can provide new opportunities to incorporate LiDAR data into forest inventory workflows. Single photon LiDAR (SPL) enables efficient, large area data acquisition and merits further investigation for forest inventory applications. Herein, we investigated the capacity of leaf-on SPL data, combined with 269 field plots, for estimating forest inventory attributes in the Great Lakes–St. Lawrence mixedwood forests of southern Ontario, Canada. Inventory attribute estimates were validated at the stand level using independent reference data acquired for 27 intensively sampled stands. Top height, Lorey’s height, gross total volume for merchantable stems, merchantable stem volume, basal area, quadratic mean diameter, and total aboveground biomass were estimated with a relative RMSE of 13.52%, 7.24%, 14.61%, 16.27%, 14.42%, 12.25%, and 11.72%, respectively. Relative bias was < 1% for all attributes except top height (10.34%), merchantable volume (3.37%), and basal area (1.68%). Accuracy and bias varied by forest type and stand-level validation was important for assessing model performance in different stand conditions. SPL data can be used to generate accurate, area-based forest inventories in mixedwood forests that have a multitude of tree species and complex forest management histories.