Resolution Lidar Research Articles

SOHD-LOAM: Suppress odometry height drift lidar odometry and mapping on undulating road

In the process of mapping outdoor undulating and flat roads, existing LiDAR SLAM systems often encounter issues such as map distortion and ghosting. These problems arise due to the low vertical resolution of multi-line LiDAR, which easily leads to the occurrence of odometry height drift during the mapping process. To address this challenge, this study propose a novel LiDAR SLAM system named SOHD-LOAM, designed specifically to suppress odometry height drift. This system encompasses several critical components, including data preprocessing, front-end LiDAR odometry, back-end LiDAR mapping, loop detection, and graph optimization. SOHD-LOAM leverages the road gradient limitation algorithm and the height smoothing algorithm as its core, while also integrating the Kalman filter, loop detection, and graph optimization techniques. To evaluate the performance of SOHD-LOAM, the comprehensive experiments are conducted with using KITTI datasets and real-world scenes. The experimental results demonstrate that SOHD-LOAM achieves superior accuracy and robustness in global odometry compared to the state-of-the-art LEGO-LOAM. Specifically, the height error of the sequences 00, 05 experiment was found to be 40.62% and 61.92% lower than that of LEGO-LOAM. Additionally, the maps generated by SOHD-LOAM exhibit no distortion or ghosting, thereby significantly enhancing map quality.

A Low-Cost High-Resolution Solid-State Lidar With Wavelength Division Multiplexed Components and Interleaved Orthogonal Polarization Grating Couplers

Laser diodearray and photo diode array combined through coarse wavelength division multiplexed (CWDM) are widely used in short-reach optical communication within massive web-scale data center. Those components can be used in solid-state photonics-based Lidar built upon silicon-on-insulator (SOI) waveguide as well, achieving significant cost saving. Beam steering can be naturally achieved through diffraction in output grating couplers. However, current design is hampered by low angular resolution. We demonstrate a novel design by combining outputs of two grating couplers with orthogonal polarizations in an interleaving manner. This scheme effectively doubles the angular resolution of on-chip Lidar.

Predicting Aerosol Extinction Coefficient With LiDAR Data Based on Deep Belief Network

Aerosol extinction coefficient (AEC) is a crucial factor of aerosol optical properties that have an important influence on the atmospheric process. As a combination of laser technology and radar technology, LiDAR is a powerful remote sensing technology for aerosol measurement. However, many uncertainties still exist in LiDAR data retrieval because frequently used retrieval methods need some assumptions or complex numerical operations for retrieving AEC, which may cause large deviation. In this letter, combining the advantages and disadvantages of Mie scattering LiDAR and high spectral resolution LiDAR (HSRL), a new inversion model based on deep belief network (DBN) is proposed to predict AEC, which can effectively avoid the uncertainty caused by many assumptions and improve the detection accuracy of Mie scattering LiDAR. The experiment results indicate that the trained DBN model is robust and satisfactory and provides a competitive solution for predicting AEC of Mie scattering LiDAR. In addition, this letter reveals that deep learning method has huge potential for LiDAR data retrieval.

Flash LiDAR-Based Super-Resolution Mapping for Small Solar System Body Exploration

Safe spacecraft navigation on small solar system bodies requires an accurate and high-resolution representation of the surface. This work develops a super-resolution mapping algorithm based on flash LiDAR measurements for unstructured terrain. It integrates a novel approach of processing multiple range values per pixel per measurement to increase the measurement redundancy and thus the map accuracy. The proposed algorithm is thus able to overcome the limitations of the low flash LiDAR resolution. Micro- and macro-motion strategies are recommended to guarantee subpixel displacements crucial for the super-resolution algorithm, and measurement constraints are introduced to ensure high measurement quality. Computational experiments based on an advanced Flash LiDAR model in a realistic simulation environment of comet 67P/Churyumov-Gerasimenko compare the resulting digital elevation model (DEM) with a baseline DEM and demonstrate the advantages of our algorithm.

High-precision beam array scanning system based on Liquid Crystal Optical Phased Array and its zero-order leakage elimination

In order to improve the scanning efficiency of lidar, a set of high precision scanning system with 40 × 40 sub-beams is established, in which a liquid crystal optical phased array is used to load the phase calculated by Gerchberg–Saxton(GS) algorithm to split the incident plane wave. Technically, the imperfections of GS algorithm often lead to problems such as sidelobes and uneven intensity of output spots, which eventually result in poor pointing accuracy of sub-beams and low scanning resolution of lidar. To solve those problems, we propose an optical method rather than the classical algorithm optimization methods, which restricts the preset sub-beams to the diffraction limit to unify the actual sub-spot morphology and ensure high pointing accuracy. In this way, the standard deviation of pointing error eventually decreases from 42 μrad to 16 μrad. In the aspect of eliminating zero-order leakage, the zero-order light leakage cannot be separated by the conventional tilt method in the case that the spatial spectrum span of the generated beam array exceeds the cut-off frequency. Concerning this issue, a novel zero-order leakage elimination method is proposed to avoid zero-order leakage by splicing two half beam array of adjacent diffraction orders on both sides of the cut-off frequency. Meanwhile, the intensity correction is carried out to achieve leakage avoidance without damaging the intensity uniformity of the beam array.

3D structural vibration identification from dynamic point clouds

Video-based measurement has received increased attention for modal analysis and nondestructive evaluation, playing an important role in the development of the next-generation structural sensing technologies. As these techniques have evolved, more quantitative approaches based on computer vision techniques have emerged on full-field unsupervised structural identification, exploiting the benefits provided by the use of video cameras such as high spatial sensor density and low installation costs. More recent work has started to explore the use of laser point cloud data for 3D mapping of scenes and structures. Sensors such as LIDAR provide huge amounts of measurements at high spatial resolution from which it is possible to estimate accurate structural geometry for applications such as the generation of CAD models. Unfortunately to-date, the frame rate and depth resolution of LIDAR and other sensors capable of 3D geometry measurements has not been sufficient for measuring structural dynamics. In this paper, we introduce an approach for efficient and extremely high resolution 3D structural dynamic identification/modal analysis from point cloud data acquired using a commercial, low-cost, time-of-flight imager. Vibration mode shapes and modal coordinates are extracted from this data by creating virtual Lagrangian sensors based on the point clouds parameters. First, time-varying point cloud data are collected from a vibrating structure. Then, a mesh of virtual sensors is created based on the dynamic point cloud data for tracking the structure’s displacement over time. Next solutions to the blind source separation problem are employed to estimate high resolution 3D mode shapes, modal coordinates, and resonant frequencies. We demonstrate the potential of our proposed approach on laboratory tests and compare the results to the data collected from conventional laser displacement sensors. This technique represents an advance towards efficiently exploring the full advantages of using dynamic point cloud data for practical monitoring applications and has the potential to be extended for a wide range of 3D motion decomposition problems.

SETUP OF A CORONA CAMERA AND IMAGE CO-REGISTRATION / CALIBRATION

Abstract. Power lines are an important infrastructure and need special attention. Their functionality is of high importance for our life. Their monitoring is important to guarantee their sustainable operation. It is done mostly by aerial survey using different technologies. In addition to standard setups, e.g. LiDAR and high resolution RGB cameras, often UV cameras, so called Corona Cameras, are used to detect unwanted discharges. In many cases this UV cameras are combined along with RGB cameras with a similar field of view in order to superimpose the detected UV signals onto a visual image of the inspected objects. Discharges at power lines indicate findings that should be monitored with such additional sensors like thermal or high resolution mapping cameras then.The use of Corona-Cameras for such inspection work needs several steps of calibration, co registering and direct referencing methods in order to get them in a good georeference to the other sensors.

Investigation of the possibilities of increasing the resolution of lidars in the infrared wavelength range for monitoring methane in the atmosphere

The paper deals with the resolution of an optical lidar for measuring the background concentration of methane in the atmosphere both on surface horizontal paths with reflection of an optical signal from natural objects and on vertical paths, when an optical signal is reflected from cloud layers with extrapolation to low space orbits. It was shown using simulations that in the case of measurements from a low space orbit, the signal accumulation time can be reduced to 1 second or less due to the large signal attenuation at the center of the absorption line in the atmospheric air column. For a signal accumulation time of 1 s, the horizontal resolution along the horizontal coordinate will be ∼8 km at a satellite orbital velocity of ∼8 km / s. The vertical resolution for measurements from space orbit can be ∼ 1 km if the accuracy of measuring the absorption line broadening is ∼0.006 nm (10%). On the horizontal paths, the experimental results are in good agreement with the simulation results. Broadening the emission line of the lidar transmitter by a factor of ∼3 makes it possible to increase the radiated power at the considered wavelength ∼ 1653 nm, improve the energy potential of the device, and bring the horizontal resolution to 5-10 meters on the speed of lidar beam, corresponding to the speed of the helicopter.

High energy LiDAR source for long distance, high resolution range imaging

AbstractHigh energy, high repetition rates laser with excellent beam quality are fundamental components for LiDAR. In particular, high pulse energy is benefit for long distance range imaging. Narrow pulse width and high repetition rates are benefit for high resolution and high refresh rate operation of LiDAR. However, increasing the laser repetition rates will cause the pulse energy decreasing and the pulse width expanding. In order to achieve the requirements at the same time, we propose a 885 nm laser diode end‐pumped RTP Q‐switched Nd:YAG laser. Pulse pumping scheme was used to avoid heat accumulation. With a master oscillator power amplifier (MOPA) system, 40.5 mJ, 7.5 ns of 1064 nm pulses were obtained at 2 kHz. The beam quality factor M2 was measured as 1.60/1.51. Moreover, by frequency doubling with a LBO crystal, 16.4 mJ, 6.5 ns of 532 nm laser with the M2 factor of 1.52 and 1.50 was obtained. The 1064 and 532 nm pulses are suitable for avalanche photodiode arrays LiDAR and streak tube imaging LiDAR, respectively. The laser system is shown to accomplish high stability from temperature of −10°C to 30°C and vibration frequency of 10 to 100 Hz.

Modeling and Experimental Study of Lidar Resolution to Determine Methane Concentration in the Earth’s Atmosphere

This article discusses the resolution of an optical lidar for measuring the background concentration of methane in the Earth’s atmosphere. Using simulation and experimental studies, it has been shown that a resolution the width of the absorption line of which is ~0.006 nm and that has a concentration of methane of ~6% of the background can be obtained with a signal-to-noise ratio for received signals of ~200. When extrapolating the results to orbital measurements, the horizontal resolution will be ~8 km and the vertical resolution will be ~1.5 km. A broadening of the emission line of the lidar transmitter by a factor of 2–3 does not significantly reduce the resolution of the lidar, which makes it possible to increase the radiated power at the considered wavelength of ~1653 nm and improve the energy potential of the device as a whole.

Airborne LiDAR and high resolution multispectral data integration in Eucalyptus tree species mapping in an Australian farmscape

Rapid decline and death of rural Eucalypts trees of all ages and species have been reported in the farmscapes of regional Australia due to various environmental and farming management related factors. The identification of existing farm tree species is important for long term management strategies to provide ecosystem stability in the region. This study explored the feasibility of structural attributes of LiDAR and spectral and spatial characteristics of high resolution remote sensing data to identify and map Eucalyptus tree species. An object based image segmentation and rule-based classification algorithm were developed to delineate tree boundaries and species classification. The integration of two datasets improved the classification accuracy (65%) against their separate classification (52% and 41%, respectively). The identification of tree species will help in getting first-hand information on existing farm trees, which may be used in assessing tree condition in time series related to management practices and complex dieback problem.

High resolution stimulated Brillouin scattering lidar using Galilean focusing system for detecting submerged objects

To improve the performance of stimulated Brillouin scattering (SBS) lidar system, a new focusing system based on Galilean telescope was designed, and the influences of focusing system on the resolution of SBS lidar were investigated theoretically and experimentally. The experiments for real-time detecting submerged object were carried out in a 100 m-long water pool. The results indicate that the focusing system can improve greatly the resolution of SBS lidar system to 0.1 m at the detecting distance of 5 m, and less than 0.5 m within the detection distances of 30 m. The attenuation coefficient of water was 0.38 m−1, and the detection distance of SBS lidar in this experiment exceeds 11 attenuation lengths. The experimental results agree well with the theoretical simulations.

LiDAR resolution for catchment-inclusive hydrodynamic models

Hydrodynamic models are usually based on LiDAR but there is little information on the LiDAR resolution required for appropriate model accuracy. In this study, algorithms to prepare DEM and roughness grids suitable for hydrodynamic modelling of both catchment and floodplain are applied to low, medium and high point-density LiDAR. The medium resolution LiDAR (9 points/m2) provided elevation and roughness grids sufficiently accurate for hydrodynamic flood mapping of urban and rural floodplains. Low-resolution LiDAR (3 points/m2) is considered adequate for hill catchments. Attention is required where narrow-crested control structures exist. Mapping and upscaling are discussed.

CO2-Tea pulse clipping using pulsed high voltage preionization for high spatial resolution I.R. Lidar systems

An extra-cavity CO2-TEA laser pulse clipper for high spatial resolution atmospheric monitoring is presented. The clipper uses pulsed high voltageto facilitate the breakdown of the gas within the clipper cell. Complete extinction of the nitrogen tail, that degrades the range resolution of LIDARS, is obtained at pressures from 375 up to 1500 Torr for nitrogen and argon gases whereas an attenuation coefficient of almost 102 is achieved for helium. Excellent energy stability and pulse width repeatability were achieved using high voltage pre-ionized gas technique.

Optimizing spaceborne LiDAR and very high resolution optical sensor parameters for biomass estimation at ICESat/GLAS footprint level using regression algorithms

Forests sequester large quantity of carbon in their woody biomass and hence accurate estimation of forest biomass is extremely crucial. The present study aims at combining information from spaceborne LiDAR (ICESat/GLAS) and high resolution optical data to estimate forest biomass. Estimation of aboveground biomass (AGB) at ICESat/GLAS footprint level was done by integrating data from multiple sensors using two regression algorithms, viz. random forest (RF) and support vector machine (SVM). The study used forest height and canopy return ratio (rCanopy) for determination of effective size of ICESat/GLAS footprints for field data collection. The forest height was predicted with root mean square error (RMSE) of 1.35 m. The study showed that six most important parameters derived from LiDAR, and passive optical data were able to explain 78.7% (adjusted) variation in the observed AGB with an RMSE of 13.9 Mg ha–1. It was also observed that 15 most important parameters were able to explain 83% (adjusted) variation in the observed AGB. It was found that SVM regression algorithm explained 88.7% of variation in AGB with an RMSE of 13.6 Mg ha–1 on the combined datasets while RF regression algorithm explained 83.5% of variation in AGB with an RMSE of 20.57 Mg ha–1. The study demonstrated that RF regression algorithm performs equally well on datasets irrespective of the correlation of underlying variables with the predicted variable whereas SVM regression was found to perform well on those datasets which had a subset of underlying variables that are correlated with the predicted variable. The study highlighted that sensor integration approach is more accurate than single sensor approach in predicting the AGB.

Optimizing Variable Radius Plot Size and LiDAR Resolution to Model Standing Volume in Conifer Forests

The conventional approach to LiDAR-based forest inventory modeling depends on field sample data from fixed-radius plots (FRP). Because FRP sampling is cost intensive, combining variable-radius plot (VRP) sampling and LiDAR data has the potential to improve inventory efficiency. The overarching goal of this study was to evaluate the integration of LiDAR and VRP data. FRP and VRP plots using different basal-area factors (BAF) were colocated in 6 conifer stands near Alberta, Michigan, in the United States. A suite of LiDAR metrics was developed for 24 different resolutions at each plot location, and a number of nonparametric prediction models were evaluated to identify an optimal LiDAR resolution and an optimal scale of VRP to spatially extend the data. An FRP-based model had root mean square error (RMSE) of 31.8 m3 ha−1, whereas the top VRP-based models were somewhat less precise, with RMSE of 38.0 m3 ha−1 and 45.8 m3 ha−1 using BAF 2.06 m2 ha−1 and BAF 2.29 m2 ha−1, respectively. The optimal LiDAR resolution for the VRP data was found to be 18 m for the selected stands, and plot-level estimates based on a model using BAF 2.29 m2 ha−1 were statistically equivalent to the FRP measurements. The use of VRP data shows promise and can substitute for FRP measurements to improve efficiency.

3D-calibration of three- and four-sensor hot-film probes based on collocated sonic using neural networks

High resolution measurements of turbulence in the atmospheric boundary layer (ABL) are critical to the understanding of physical processes and parameterization of important quantities, such as the turbulent kinetic energy dissipation. Low spatio-temporal resolution of standard atmospheric instruments, sonic anemometers and LIDARs, limits their suitability for fine-scale measurements of ABL. The use of miniature hot-films is an alternative technique, although such probes require frequent calibration, which is logistically untenable in field setups. Accurate and truthful calibration is crucial for the multi-hot-films applications in atmospheric studies, because the ability to conduct calibration in situ ultimately determines the turbulence measurements quality. Kit et al ( J. Atmos. Ocean. Technol. 27 23–41) described a novel methodology for calibration of hot-film probes using a collocated sonic anemometer combined with a neural network (NN) approach. An important step in the algorithm is the generation of a calibration set for NN training by an appropriate low-pass filtering of the high resolution voltages, measured by the hot-film-sensors and low resolution velocities acquired by the sonic. In Kit et al ( J. Atmos. Ocean. Technol. 27 23–41), Kit and Grits ( J. Atmos. Ocean. Technol. 28 104–10) and Vitkin et al ( Meas. Sci. Technol. 25 75801), the authors reported on successful use of this approach for in situ calibration, but also on the method’s limitations and restricted range of applicability. In their earlier work, a jet facility and a probe, comprised of two orthogonal x-hot-films, were used for calibration and for full dataset generation. In the current work, a comprehensive laboratory study of 3D-calibration of two multi-hot-film probes (triple- and four-sensor) using a grid flow was conducted. The probes were embedded in a collocated sonic, and their relative pitch and yaw orientation to the mean flow was changed by means of motorized traverses. The study demonstrated that NN-calibration is a powerful tool for calibration of multi-sensor 3D-hot film probes embedded in a collocated sonic, and can be employed in long-lasting field campaigns.

Calibration and Validation of Landsat Tree Cover in the Taiga−Tundra Ecotone

Monitoring current forest characteristics in the taiga−tundra ecotone (TTE) at multiple scales is critical for understanding its vulnerability to structural changes. A 30 m spatial resolution Landsat-based tree canopy cover map has been calibrated and validated in the TTE with reference tree cover data from airborne LiDAR and high resolution spaceborne images across the full range of boreal forest tree cover. This domain-specific calibration model used estimates of forest height to determine reference forest cover that best matched Landsat estimates. The model removed the systematic under-estimation of tree canopy cover >80% and indicated that Landsat estimates of tree canopy cover more closely matched canopies at least 2 m in height rather than 5 m. The validation improved estimates of uncertainty in tree canopy cover in discontinuous TTE forests for three temporal epochs (2000, 2005, and 2010) by reducing systematic errors, leading to increases in tree canopy cover uncertainty. Average pixel-level uncertainties in tree canopy cover were 29.0%, 27.1% and 31.1% for the 2000, 2005 and 2010 epochs, respectively. Maps from these calibrated data improve the uncertainty associated with Landsat tree canopy cover estimates in the discontinuous forests of the circumpolar TTE.

OBJECT BASED IMAGE ANALYSIS COMBINING HIGH SPATIAL RESOLUTION IMAGERY AND LASER POINT CLOUDS FOR URBAN LAND COVER

With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clouds. The advanced workflow for urban land cover is designed with four components. Firstly, colour-infrared TrueOrtho photo and laser point clouds were pre-processed to derive the parcel map of water bodies and nDSM respectively. Secondly, image objects are created via multi-resolution image segmentation integrating scale parameter, the colour and shape properties with compactness criterion. Image can be subdivided into separate object regions. Thirdly, image objects classification is performed on the basis of segmentation and a rule set of knowledge decision tree. These objects imagery are classified into six classes such as water bodies, low vegetation/grass, tree, low building, high building and road. Finally, in order to assess the validity of the classification results for six classes, accuracy assessment is performed through comparing randomly distributed reference points of TrueOrtho imagery with the classification results, forming the confusion matrix and calculating overall accuracy and Kappa coefficient. The study area focuses on test site Vaihingen/Enz and a patch of test datasets comes from the benchmark of ISPRS WG III/4 test project. The classification results show higher overall accuracy for most types of urban land cover. Overall accuracy is 89.5% and Kappa coefficient equals to 0.865. The OBIA approach provides an effective and convenient way to combine high resolution imagery and Lidar ancillary data for classification of urban land cover.

The Two‐Column Aerosol Project: Phase I—Overview and impact of elevated aerosol layers on aerosol optical depth

AbstractThe Two‐Column Aerosol Project (TCAP), conducted from June 2012 through June 2013, was a unique study designed to provide a comprehensive data set that can be used to investigate a number of important climate science questions, including those related to aerosol mixing state and aerosol radiative forcing. The study was designed to sample the atmosphere between and within two atmospheric columns; one fixed near the coast of North America (over Cape Cod, MA) and a second moveable column over the Atlantic Ocean several hundred kilometers from the coast. The U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) was deployed at the base of the Cape Cod column, and the ARM Aerial Facility was utilized for the summer and winter intensive observation periods. One important finding from TCAP is that four of six nearly cloud‐free flight days had aerosol layers aloft in both the Cape Cod and maritime columns that were detected using the nadir pointing second‐generation NASA high‐spectral resolution lidar (HSRL‐2). These layers contributed up to 60% of the total observed aerosol optical depth (AOD). Many of these layers were also intercepted by the aircraft configured for in situ sampling, and the aerosol in the layers was found to have increased amounts of biomass burning material and nitrate compared to aerosol found near the surface. In addition, while there was a great deal of spatial and day‐to‐day variability in the aerosol chemical composition and optical properties, no systematic differences between the two columns were observed.