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A CMOS SPAD Imager with Collision Detection and 128 Dynamically Reallocating TDCs for Single-Photon Counting and 3D Time-of-Flight Imaging
摘要： Per-pixel time-to-digital converter (TDC) architectures have been exploited by single-photon avalanche diode (SPAD) sensors to achieve high photon throughput, but at the expense of fill factor, pixel pitch and readout efficiency. In contrast, TDC sharing architecture usually features high fill factor at small pixel pitch and energy efficient event-driven readout. While the photon throughput is not necessarily lower than that of per-pixel TDC architectures, since the throughput is not only decided by the TDC number but also the readout bandwidth. In this paper, a SPAD sensor with 32 × 32 pixels fabricated with a 180 nm CMOS image sensor technology is presented, where dynamically reallocating TDCs were implemented to achieve the same photon throughput as that of per-pixel TDCs. Each 4 TDCs are shared by 32 pixels via a collision detection bus, which enables a fill factor of 28% with a pixel pitch of 28.5 μm. The TDCs were characterized, obtaining the peak-to-peak differential and integral non-linearity of ?0.07/+0.08 LSB and ?0.38/+0.75 LSB, respectively. The sensor was demonstrated in a scanning light-detection-and-ranging (LiDAR) system equipped with an ultra-low power laser, achieving depth imaging up to 10 m at 6 frames/s with a resolution of 64 × 64 with 50 lux background light.
关键词： image sensor,light detection and ranging,time-of-flight,SPAD,time-to-digital converter,LiDAR,single-photon avalanche diode,collision detection bus,dynamic reallocation
更新于2025-09-19 17:15:36
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A Turbulence-Oriented Approach to Retrieve Various Atmospheric Parameters Using Advanced Lidar Data Processing Techniques
摘要： The article is aimed at presenting a semi-empirical model coded and computed in the programming language Python, which utilizes data gathered with a standard biaxial elastic lidar platform in order to calculate the altitude profiles of the structure coefficients of the atmospheric refraction index C2N(z) and other associated turbulence parameters. Additionally, the model can be used to calculate the PBL (Planetary Boundary Layer) height, and other parameters typically employed in the field of astronomy. Solving the Fernard–Klett inversion by correlating sun-photometer data obtained through our AERONET site with lidar data, it can yield the atmospheric extinction and backscatter profiles α(z) and β(z), and thus obtain the atmospheric optical depth. Finally, several theoretical notions of interest that utilize the solved parameters are presented, such as approximated relations between C2N(z) and the atmospheric temperature profile T(z), and between the scintillation of backscattered lidar signal and the average wind speed profile U(z). These obtained profiles and parameters also have several environmental applications that are connected directly and indirectly to human health and well-being, ranging from understanding the transport of aerosols in the atmosphere and minimizing the errors in measuring it, to predicting extreme, and potentially-damaging, meteorological events.
关键词： RCS,temperature profile,structure coefficients,environment,human health,atmospheric extinction,atmospheric backscatter,wind speed profile,lidar,turbulence
更新于2025-09-19 17:15:36
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A depolarisation lidar-based method for the determination of liquid-cloud microphysical properties
摘要： The fact that polarisation lidars measure a depolarisation signal in liquid clouds due to the occurrence of multiple scattering is well known. The degree of measured depolarisation depends on the lidar characteristics (e.g. wavelength and receiver field of view) as well as the cloud macrophysical (e.g. cloud-base altitude) and microphysical (e.g. effective radius, liquid water content) properties. Efforts seeking to use depolarisation information in a quantitative manner to retrieve cloud properties have been undertaken with, arguably, limited practical success. In this work we present a retrieval procedure applicable to clouds with (quasi-)linear liquid water content (LWC) profiles and (quasi-)constant cloud-droplet number density in the cloud-base region. Thus limiting the applicability of the procedure allows us to reduce the cloud variables to two parameters (namely the derivative of the liquid water content with height and the extinction at a fixed distance above cloud base). This simplification, in turn, allows us to employ a fast and robust optimal-estimation inversion using pre-computed look-up tables produced using extensive lidar Monte Carlo (MC) multiple-scattering simulations. In this paper, we describe the theory behind the inversion procedure and successfully apply it to simulated observations based on large-eddy simulation (LES) model output. The inversion procedure is then applied to actual depolarisation lidar data corresponding to a range of cases taken from the Cabauw measurement site in the central Netherlands. The lidar results were then used to predict the corresponding cloud-base region radar reflectivities. In non-drizzling condition, it was found that the lidar inversion results can be used to predict the observed radar reflectivities with an accuracy within the radar calibration uncertainty (2–3 dBZ). This result strongly supports the accuracy of the lidar inversion results. Results of a comparison between ground-based aerosol number concentration and lidar-derived cloud-droplet number densities are also presented and discussed. The observed relationship between the two quantities is seen to be consistent with the results of previous studies based on aircraft-based in situ measurements.
关键词： Monte Carlo simulations,cloud-base region,aerosol-cloud interactions,retrieval procedure,depolarisation lidar,liquid-cloud microphysical properties,multiple scattering,radar reflectivity
更新于2025-09-19 17:15:36
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Modelling the effects of fundamental UAV flight parameters on LiDAR point clouds to facilitate objectives-based planning
摘要： Utilised globally across a wide range of applications, the ability to assess and understand LiDAR system capabilities represents an essential component in developing informed decisions on instrument selection and the logistical planning processes associated with site-specific limitations, project objectives and UAV operations. This study employed the new SLAM-based CSIRO 'Hovermap' LiDAR system within a purpose-built environment as a testbed to experimentally investigate the interactive effects of fundamental UAV flight parameters on key metrics of LiDAR point clouds. Assessed within a full factorial design at both Site- and Target-levels, the UAV input variables of Pattern, ground Speed and above ground Altitude (AGL) were tested against the point cloud response variables Density, GSD and Accuracy as measured by RMSE and cloud-to-mesh Euclidian distance ('Deviation'). A novel approach is described wherein the trajectory file of each flight was examined to determine the observed values of the input and response variables, remove noise and facilitate a standardised basis of comparison. Several new terms are introduced including Sampling Effort Variable (SEV, s?m?2), Effective Scan Rate (ESR, pts?s?1) and Effective Density Rate (EDR, pts?m?2?s?1) as well as an alternate approach to describe Pattern (s?m?1) as a scalar quantity. Reporting significant effects with all response variables at both Site- and Target-levels, the Range of the LiDAR sensor, closely associated with Altitude, presented as the single most important factor. Interestingly, the combination of the independent variables as SEV and EDRpred ('predicted' EDR) showed the highest coefficient of determination in the Site-level prediction of Density (AdjR2 = 0.894) and GSD (AdjR2 = 0.978,), respectively, whilst Range best correlated with observed RMSE (AdjR2 = 0.948) and Deviation (AdjR2 = 0.963). Predictive models returned mixed results when evaluated at the Target-level and highlights the need for further investigation to achieve the maximum benefit of high-resolution UAV LiDAR. The results presented here confirm that the CSIRO Hovermap performance is robust and, although variable depending on UAV flight parameters, is predictable and demonstrates the potential value in understanding system performance in harmonised flight planning to achieve project-specific objectives.
关键词： Drone,Mapping,LiDAR,UAV,Hovermap
更新于2025-09-19 17:15:36
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Optimized weighting function for IPDA lidar concerning the lower layer CO2 concentration fluctuation
摘要： Weighting function can affect the retrieval of the column gas mixing ratio of integrated path differential absorption (IPDA) lidar because it contains the vertical distribution information of the target gas. In order to optimize the wavelengths within the 1.57 um spectral band for IPDA lidar for CO2 measurement. The temperature, pressure and water vapor (TPH) sensitivity and the proportion of the weight function integration in the lower layer part to the total atmosphere layer (R) of each wavelength are calculated. The measurement errors of each wavelength are simulated with CO2 fluctuation in the lower layer. The specific wavelength with the R value of 0.6 has been found to be a stable measurement bias which does not change with fluctuations, so the R value of 0.6 is used to optimize the wavelength for measurement CO2. The simulation results show that the measurement error via wavelength is smaller than 0.05 ppm, when the TPH sensitivity is smaller than 0.95 and when R is about 0.6. This proposed method can be applicable for the further airborne or space-borne lidar development.
关键词： Column mixing ratio of CO2,Weighting function,Differential absorption lidar,IPDA
更新于2025-09-19 17:15:36
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Can a city reach energy self-sufficiency by means of rooftop photovoltaics? Case study from Poland
摘要： The process of decarbonising economies has to take place on a multiple levels. One of the objectives is to ensure renewables-based energy self-sufficiency of cities. Cities have become home to the majority of the world’s population, and at the same time contribute enormously to environmental pollution. Considering the above, the purposes of this paper are threefold: to formulate a methodology for estimating rooftop photovoltaics (PV) potential in urban areas based on detailed Light Detection And Ranging (LiDAR) data; to calculate the spatial variability of load and photovoltaics energy supply, and thus to distinguish zones with various levels of energy self-sufficiency; and finally, to scrutinise the economic and environmental aspects of such a solution in given conditions. Wroc?aw, the capital city of the Lower Silesia voivodeship (NUTS 2 administrative division) in south-west Poland (Central Europe), was selected as a case study. The city has a population of close to 650,000 and an annual electricity consumption slightly exceeding 2.2 TWh. Industry constitutes 46% of that demand, and households 31%. The results show that up to 850 MW of rooftop PV can be installed in the city, which has the potential to reduce the electrical energy related emissions by almost 30% and simultaneously to increase the city’s energy self-sufficiency. Although energy storage, in the form of batteries, slightly improves both the autarky and environmental indices, the relation between potential PV generation and load makes them very infrequently useful (mostly in summer) and not economically justified.
关键词： renewable energy,power system transition,LiDAR,GIS,self-sufficiency
更新于2025-09-19 17:13:59
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Comparing canopy height estimates from satellite-based photogrammetry, airborne laser scanning and field measurements across Australian production and conservation eucalypt forests
摘要： Airborne Laser Scanning (ALS) generates accurate data for calculating forest metrics, such as canopy height, yet can be cost-prohibitive. Satellite-based stereo pair photogrammetry has the potential to overcome this limitation of ALS to facilitate multi-temporal change analysis when ALS data capture is unfeasible; however, it remains largely untested across Australian conservation and production eucalypt forests. This study examined root-mean-square differences (RMSD) between canopy height measurements derived from ALS, field measurements and satellite-based photogrammetry for a spotted gum (Corymbia citriodora) plantation and scribbly gum (Eucalyptus racemosa) woodland in south-east Queensland, Australia. The comparison found satellite-based photogrammetry under predicted canopy height compared to field measurements and ALS, whilst the RMSD indicated low performance for satellite-based photogrammetry across the eucalypt plantation and woodland. The open and heterogenous forest structure typical in eucalypt forests combined with low point cloud density for photogrammetry to inadequately sample the canopy and increase stereo matching errors; which was exacerbated across the open and heterogenous scribbly gum woodland. Current satellite-based photogrammetry is therefore unlikely to provide a viable alternative to ALS when analysing canopy height across eucalypt forests at high-resolution. General surface analysis across large areas of eucalypt forest at moderate resolution, or airborne photogrammetric methods, could demonstrate increased viability as an alternative to ALS.
关键词： forest inventory,CHM,worldview-2,stereo pair,LiDAR
更新于2025-09-19 17:13:59
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Forest Road Status Assessment Using Airborne Laser Scanning
摘要： Forest roads allow access for silvicultural operations, harvesting, recreational activities, wildlife management, and fire suppression. In British Columbia, Canada, roads that are no longer required must be deactivated (temporarily, semipermanently, or permanently) in order to minimize the impact on the overall forested ecosystem. However, the remoteness and size of the road network present challenges for monitoring. Our aim was to examine the utility of airborne laser scanning data to assess the status and quality of forest roads across 52,000 hectares of coastal forest in British Columbia. Within the forest estate, roads can be active or deactivated, or have an unknown status. We classified road segments based on the vegetation growth on the road surface, and edges, by classifying the height distribution of airborne laser scanning returns within each road segment into four groups: no vegetation, minor vegetation, dense understory vegetation, and dense overstory vegetation. Validation indicated that 73 percent of roads were classified correctly when compared to independent field observations. The majority were classified as active roads with no vegetation or deactivated with dense vegetation. The approach presented herein can aid forest managers in verifying the status of the roads in their management area, especially in remote areas where field assessments are costly and time-consuming.
关键词： forest management,forestry,road quality,ALS,land use,LiDAR,road classification
更新于2025-09-19 17:13:59
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Mahendraparvata: an early Angkor-period capital defined through airborne laser scanning at Phnom Kulen
摘要： Inscriptional evidence suggests that the Phnom Kulen plateau to the north-east of Angkor in Cambodia was the location of Mahendraparvata—an early Angkorian capital city and one of the first capitals of the Khmer Empire (ninth to fifteenth centuries AD). To date, however, archaeological evidence has been limited to a scatter of small and apparently isolated shrines. Here, the authors combine airborne laser scanning with ground-based survey to define an extended urban network dating from the ninth century AD, which they identify as Mahendraparvata. This research yields new and important insights into the emergence of Angkorian urban areas.
关键词： Mahendraparvata,Khmer,lidar,Southeast Asia,urbanism,Angkor
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE Intelligent Transportation Systems Conference - ITSC - Auckland, New Zealand (2019.10.27-2019.10.30)] 2019 IEEE Intelligent Transportation Systems Conference (ITSC) - Training a Fast Object Detector for LiDAR Range Images Using Labeled Data from Sensors with Higher Resolution
摘要： In this paper, we describe a strategy for training neural networks for object detection in range images obtained from one type of LiDAR sensor using labeled data from a different type of LiDAR sensor. Additionally, an efficient model for object detection in range images for use in self-driving cars is presented. Currently, the highest performing algorithms for object detection from LiDAR measurements are based on neural networks. Training these networks using supervised learning requires large annotated datasets. Therefore, most research using neural networks for object detection from LiDAR point clouds is conducted on a very small number of publicly available datasets. Consequently, only a small number of sensor types are used. We use an existing annotated dataset to train a neural network that can be used with a LiDAR sensor that has a lower resolution than the one used for recording the annotated dataset. This is done by simulating data from the lower resolution LiDAR sensor based on the higher resolution dataset. Furthermore, improvements to models that use LiDAR range images for object detection are presented. The results are validated using both simulated sensor data and data from an actual lower resolution sensor mounted to a research vehicle. It is shown that the model can detect objects from 360? range images in real time.
关键词： self-driving cars,object detection,LiDAR,range images,neural networks
更新于2025-09-19 17:13:59