Traditional Lidar Research Articles

Constrained Retrievals of Aerosol Optical Properties Using Combined Lidar and Imager Measurements During the FIREX-AQ Campaign

Smoke aerosols arise from a variety of regional sources and fuel types dependent on the properties of the fire, leading to spatial variability in smoke composition and optical properties. After emission, these aerosols age and mix in the atmosphere with other aerosol species, such as sulfates, altering the optical, and microphysical properties of the smoke aerosols over time. Thus, lidar ratio (extinction to backscatter ratio) derived from lidar sensors exhibit spatiotemporal variability for smoke. Traditional backscatter lidar processing algorithms employ a signal loss method that utilizes the reduction of signals below and above cloud layers, enabling simultaneous retrievals of both layer-averaged lidar ratio and particulate extinction, which avoids the need for assigning lidar ratios based on layer type as is typically used for backscatter lidar algorithms. In this study, the signal loss method, which is traditionally designed for cloud property retrievals, is attempted for elevated smoke plume property retrievals using NASA’s Cloud Physics Lidar (CPL) observations from the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Good agreement (linear correlation coefficient of 0.67) is found between aerosol optical depth (AOD) derived from the signal loss method and the constrained method, utilizing collocated GOES MAGARA AOD values as constraints for lidar ratio retrievals, for the Williams Flats smoke event. Differences in derived lidar ratios from the signal loss method and the constrained method (13.6 and 7.4%) are found to be smaller than the expected signal loss lidar ratio error estimate of ∼17–23%. A good agreement is also found in lidar ratios derived from this study and from using Differential Absorption Lidar-High Spectral Resolution Lidar (DIAL-HSRL) measurements for the Williams Flats Fire. The lidar ratio statistics of smoke plumes presented in this analysis (51 ± 13 sr) also compare favorably with lidar ratio values found in previous studies; however, they remain lower than the assumed smoke lidar ratio of 70 sr (at 532 nm) used by CALIPSO and CPL, and vary with plume transport distance. These findings suggest lidar ratio is likely to be regionally specific and evolve with plume transport. Thus, innovative methods for simultaneous retrieval of lidar ratio and aerosol extinction, such as the signal loss method proposed in this study, are needed for accurate aerosol retrievals from standard backscatter lidars in the future.

Multipoint Combined Processing for FMCW LiDAR

This paper proposes a multipoint processing method (MCP) for FMCW LiDAR. The proposed method reduces the number of fast Fourier transform (FFT) calculation compared to the traditional FMCW LiDAR method. This leads to a reduced power consumption. To end this, the proposed method accumulates the multiple beat frequencies in the time domain before the FMCW LiDAR extracts the measured distances from the beat frequencies using the fast Fourier transform (FFT). In order to distinguish the distances that are extracted from the FFT operation, a pulse-width modulation (PWM) counter and an analog comparator were applied. A hysteresis analog comparator was used to increase the immunity from the external noise. When the measured distances are too close to be indistinguishable by the FFT, it may fail to distinguish the measurement order. In order to respond to this situation, the proposed method includes a point-calibration algorithm. The root mean square error (RMSE) and hardware resource usage are utilized in order to assess the performance of the proposed method. The experimental results show that the proposed method achieves power usage time savings compared to the traditional method.

Underwater LiDAR Image Enhancement Using a GAN Based Machine Learning Technique

Robust underwater imaging sensors are essential in applications such as identifying and classifying marine animals in turbid waters, typically found around coastal monitoring sites. The Unobtrusive Multi-static Serial LiDAR Imager (UMSLI) has been developed to capture images taken in degraded underwater environments and provides superior range and image contrast over conventional optical cameras. In contrast to traditional underwater LiDAR systems that employ blue-green lasers, UMLSI will not harm the vision of marine animals, which is an important aspect in such settings. As with any other underwater LiDAR sensors, improving the quality of images taken in highly turbid water is essential to the UMSLI system. This paper proposes a novel machine learning image enhancement technique based on a Generative Adversarial Network (GAN) framework. One main contribution of the method is the incorporation of a correntropy-based perceptual loss. This technique has shown to be effective in enhancing ground-based images and has been adapted for LiDAR image enhancement. Given the limitation of existing underwater LiDAR data, a method for simulating degraded data for training is also presented. This LiDAR technique was validated using LiDAR data captured by the UMSLI system within Florida Atlantic University’s Harbor Branch Oceanographic Institute (FAU-HBOI) optical test facility and at the Marine and Coastal Research Laboratory at the Pacific Northwest National Laboratory (MCRL-PNNL). This image enhancement technique can be readily extended to other underwater LiDAR systems.

Comparative Analysis of Multi-Platform, Multi-Resolution, Multi-Temporal LiDAR Data for Forest Inventory

LiDAR technology is rapidly evolving as various new systems emerge, providing unprecedented data to characterize forest vertical structure. Data from different LiDAR systems present distinct characteristics owing to a combined effect of sensor specifications, data acquisition strategies, as well as forest conditions such as tree density and canopy cover. Comparative analysis of multi-platform, multi-resolution, and multi-temporal LiDAR data provides guidelines for selecting appropriate LiDAR systems and data processing tools for different research questions, and thus is of crucial importance. This study presents a comprehensive comparison of point clouds from four systems, linear and Geiger-mode LiDAR from manned aircraft and multi-beam LiDAR on unmanned aerial vehicle (UAV), and in-house developed Backpack, with the consideration of different forest canopy cover scenarios. The results suggest that the proximal Backpack LiDAR can provide the finest level of information, followed by UAV LiDAR, Geiger-mode LiDAR, and linear LiDAR. The emerging Geiger-mode LiDAR can capture a significantly higher level of detail while operating at a higher altitude as compared to the traditional linear LiDAR. The results also show: (1) canopy cover percentage has a critical impact on the ability of aerial and terrestrial systems to acquire information corresponding to the lower and upper portions of the tree canopy, respectively; (2) all the systems can obtain adequate ground points for digital terrain model generation irrespective of canopy cover conditions; and (3) point clouds from different systems are in agreement within a ±3 cm and ±7 cm range along the vertical and planimetric directions, respectively.

Sensor Data Fusion for a Mobile Robot Using Neural Networks.

Mobile robots must be capable to obtain an accurate map of their surroundings to move within it. To detect different materials that might be undetectable to one sensor but not others it is necessary to construct at least a two-sensor fusion scheme. With this, it is possible to generate a 2D occupancy map in which glass obstacles are identified. An artificial neural network is used to fuse data from a tri-sensor (RealSense Stereo camera, 2D LiDAR, and Ultrasonic Sensors) setup capable of detecting glass and other materials typically found in indoor environments that may or may not be visible to traditional 2D LiDAR sensors, hence the expression improved LiDAR. A preprocessing scheme is implemented to filter all the outliers, project a 3D pointcloud to a 2D plane and adjust distance data. With a Neural Network as a data fusion algorithm, we integrate all the information into a single, more accurate distance-to-obstacle reading to finally generate a 2D Occupancy Grid Map (OGM) that considers all sensors information. The Robotis Turtlebot3 Waffle Pi robot is used as the experimental platform to conduct experiments given the different fusion strategies. Test results show that with such a fusion algorithm, it is possible to detect glass and other obstacles with an estimated root-mean-square error (RMSE) of 3 cm with multiple fusion strategies.

Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level

Advanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging systems. In this study, we tested the ability of HSL in terms of estimating maize N concentration at the leaf-level by using spectral indices and partial least squares regression (PLSR) methods. Subsequently, the N estimation was scaled up to the plant-level based on HSL point clouds. Biomass, extracted with structural proxies, was utilized to exhibit its supplemental effect on N concentration. The results show that HSL has the ability to extract N concentrations at both the leaf-level and the canopy-level, and PLSR showed better performance (R2 > 0.6) than the single spectral index (R2 > 0.4). In comparison to the stem height and maximum canopy width, the plant height had the strongest ability (R2 = 0.88) to estimate biomass. Future research should utilize larger datasets to test the viability of using HSL to monitor the N concentration of crops, which is beneficial for precision agriculture.

LiDAR Positioning Algorithm Based on ICP and Artificial Landmarks Assistance.

As one of the automated guided vehicle (AGV) positioning methods, the LiDAR positioning method, based on artificial landmarks, has been widely used in warehousing logistics industries in recent years. However, the traditional LiDAR positioning method based on artificial landmarks mainly depends on the three-point positioning method, the performance of which is limited due to landmarks’ layout and detection requirements. This paper proposes a LiDAR positioning algorithm based on iterative closest point (ICP) and artificial landmarks assistance. It provides improvements based on the traditional ICP algorithm. The result of positioning provided by the landmarks is used as the initial iteration ICP value. The combination of the ICP algorithm and landmarks enables the positioning algorithm to maintain a certain positioning precision when landmark detection is disturbed. By comparing the proposed algorithm with the positioning scheme developed by SICK in Germany, we prove that the combination of the ICP algorithm and landmarks can effectively improve the robustness under the premise of ensuring precision.

Correction algorithm of the frequency-modulated continuous-wave LIDAR ranging system.

Frequency-modulated continuous-wave LIDAR has broad application prospects. Compared with the traditional pulse LIDAR, the FMCW LIDAR has the advantages of high resolution and long measurement distance. But it still can be affected by several factors, including environmental noise, spectrum aliasing, spectrum leakage and other issues. Some traditional filtering algorithms or signal transformation algorithms can improve the above problems, but the effect is not ideal. This paper proposes a signal correction algorithm called the VMD-based refined cross-power spectral density algorithm (VRCPSD). This algorithm is based on signal decomposition denoising and improved spectrum refinement methods. The algorithm applies variational mode decomposition, spectrum refinement and cross-power spectral density to signal processing. The VRCPSD algorithm is compared with the traditional spectrum correction algorithm on the high-speed linear array APD FMCW LIDAR experimental platform. The results show that the VRCPSD algorithm has a better spectrum correction effect on the LIDAR experimental platform. This algorithm can reduce the margin of error to the centimeter level. Therefore, the algorithm is promising in that it can improve the signal waveform of the FMCW laser radar ranging system, make the spectrum get better correction and make the distance more accurate.

Design and modeling of multi-spectral polarimetry streak tube imaging LiDAR

A new Multi-Spectral Polarimetry Streak Tube Imaging LiDAR (MSP-STIL) system is proposed, with mathematical model of the system established and verified. Multiple experiments are conducted before drawing conclusions. Intensity images and degree of polarization (DOP) images at three wavelengths and a depth image of the target area can be obtained by the single imaging mode of the system, and Mueller matrices at three wavelengths of the target area can be obtained by the twice imaging mode of the system. Meanwhile, the proposed system utilizes the fiber optic delay device to alleviate the contradiction of traditional streak tube imaging LiDAR system among the large number of echo signals, the depth resolution and the depth of field. Therefore, the proposed system can perform multi-spectral polarimetry imaging and three-dimensional imaging with higher depth resolution and larger depth of field. Compared with the traditional single-wavelength streak tube imaging LiDAR system, the image contrast of the target area can be improved by fusing the spectral information, polarization information and three-dimensional information obtained by the proposed system, and the high-contrast image makes it easier to distinguish the target.

Enhancing Solid State LiDAR Mapping with a 2D Spinning LiDAR in Urban Scenario SLAM on Ground Vehicles.

Solid-State LiDAR (SSL) takes an increasing share of the LiDAR market. Compared with traditional spinning LiDAR, SSLs are more compact, energy-efficient and cost-effective. Generally, the current study of SSL mapping is limited to adapting existing SLAM algorithms to an SSL sensor. However, compared with spinning LiDARs, SSLs are different in terms of their irregular scan patterns and limited FOV. Directly applying existing SLAM approaches on them often increase the instability of a mapping process. This study proposes a systematic design, which consists of a dual-LiDAR mapping system and a three DOF interpolated six DOF odometry. For dual-LiDAR mapping, this work uses a 2D LiDAR to enhance a 3D SSL performance on a ground vehicle platform. The proposed system takes a 2D LiDAR to preprocess the scanning field into a number of feature sections according to the curvatures on the 2D fraction. Subsequently, this section information is passed to 3D SSL for direction feature selection. Additionally, this work proposes an odometry interpolation method which uses both LiDARs to generate two separated odometries. The proposed odometry interpolation method selectively determines the appropriate odometry information to update the system state under challenging conditions. Experiments are conducted in different scenarios. The results proves that the proposed approach is able to utilise 12 times more corner features from the environment than the comparied method, thus results in a demonstrable improvement in its absolute position error.

Ground target extraction using airborne streak tube imaging LiDAR

Airborne LiDAR has become a kind of indispensable measurement device in the current field of remote sensing. However, target extraction using traditional airborne LiDAR based on single-point scanning requires filtering and point cloud segmentation operations, which are complicated and time consuming. Although some researchers have studied streak tube imaging LiDAR (STIL) before, there are few reports in which it is used as an airborne LiDAR for ground measurement in large-scale field. We propose a method of ground target extraction using STIL. Taking advantage of the structural properties of the STIL, complex filtering and point cloud segmentation algorithms are avoided in the target extraction method. The purpose of this article is to verify the feasibility of airborne STIL in ground target extraction. We analyzed the raw streak signal image collected by field experiment and used morphology and intensity information to extract features. After that, we employed the decision tree classifier to classify the four kinds of targets and evaluated the extraction results. The results show that the target extraction achieved satisfactory consequences under an acceptable level. That demonstrates that ground target extraction using STIL is feasible in the field of large-scale remote sensing.

Unraveling the Morphological Constraints on Roman Gold Mining Hydraulic Infrastructure in NW Spain. A UAV-Derived Photogrammetric and Multispectral Approach

The province of León preserves a unique hydraulic infrastructure 1200 km-long, used for the exploitation of auriferous deposits in Roman times. It represents the most extensive waterworks in Europe and is one of the best-preserved examples of mining heritage in Antiquity. In this work, three mining exploitation sectors (upper, middle, and lower) characterized by channels and leats developed in different geological materials were examined, using Unmanned Aerial Vehicles (UAVs). A multi-approach based on a comparison of photogrammetric and multispectral data improved the identification and description of the hydraulic network. Comparison with traditional orthoimages and LiDAR data suggests that UAV-derived multispectral images are of great interest in areas where these sets of data have low resolution or areas that are densely covered by vegetation. The results showed that the size of the channel box and its width were factors that do not depend exclusively on the available water resources, as previously suggested, but also on the geological and hydraulic conditioning factors that intervene in each sector. Additionally, the detailed study allowed the establishment of a water sheet maximum height that was much lower than previously thought. All in all, these inferences might help researchers develop new strategies for mapping the Roman mining infrastructure and establishing the importance of geological inheritance on the construction of the hydraulic system that led the Romans to the accomplishment of the largest mining infrastructure ever known in Europe.

Quantum enhanced Doppler LiDAR based on integrated quantum squeezed light source(Invited)

Though the measurement precision of the traditional LiDAR is gradually increased, it is still limited by the quantum noise of the optical field and the extra noise introduced by the detection process. To improve the detection performance of LiDAR, a new scheme using quantum squeezed state light field as local oscillator of LiDAR was proposed and the key factors for improving the detection precision of LiDAR was analyzed. Then an integrated low-noise squeezed light field was prepared, and the experiment of LiDAR Doppler information measurement was carried out. The results show that the Doppler information detection sensitivity by using quantum squeezed state is 3 dB higher than the traditional scheme of the coherent state light field as the local oscillator, which paves a research path for the detection of weak signals such as Doppler information.

Accurate Road Marking Detection from Noisy Point Clouds Acquired by Low-Cost Mobile LiDAR Systems

Road markings that provide instructions for unmanned driving are important elements in high-precision maps. In road information collection technology, multi-beam mobile LiDAR scanning (MLS) is currently adopted instead of traditional mono-beam LiDAR scanning because of the advantages of low cost and multiple fields of view for multi-beam laser scanners; however, the intensity information scanned by multi-beam systems is noisy and current methods designed for road marking detection from mono-beam point clouds are of low accuracy. This paper presents an accurate algorithm for detecting road markings from noisy point clouds, where most nonroad points are removed and the remaining points are organized into a set of consecutive pseudo-scan lines for parallel and/or online processing. The road surface is precisely extracted by a moving fitting window filter from each pseudo-scan line, and a marker edge detector combining an intensity gradient with an intensity statistics histogram is presented for road marking detection. Quantitative results indicate that the proposed method achieves average recall, precision, and Matthews correlation coefficient (MCC) levels of 90%, 95%, and 92%, respectively, showing excellent performance for road marking detection from multi-beam scanning point clouds.

3-D Dense Rangefinder Sensor With a Low-Cost Scanning Mechanism

LiDAR sensors have been widely applied in autonomous robotics and autonomous systems. High-channel LiDARs or multiple low-channel LiDARs are adopted in these applications to overcome the poor vertical resolution of point clouds, as this scenario can lead to high costs. Here, as a means to improve the vertical resolution of point clouds and lower the cost, we present a 3-D dense rangefinder sensor composed of a low-channel LiDAR, a camera, a brush-less motor, and a crank-link system to replace the traditional LiDAR. A special registration method is designed to register the high-dynamic point cloud. The measurement uncertainty of this method is analyzed. In addition, a 3-D object detection method is used to obtain the 3-D pose of obstacles by combining the dense point cloud and an image-based 2-D object detection algorithm. Finally, several experiments are performed to evaluate the effectiveness of the proposed 3-D rangefinder sensor.

SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

This letter describes an end-to-end pipeline for tree diameter estimation based on semantic segmentation and lidar odometry and mapping. Accurate mapping of this type of environment is challenging since the ground and the trees are surrounded by leaves, thorns and vines, and the sensor typically experiences extreme motion. We propose a semantic feature based pose optimization that simultaneously refines the tree models while estimating the robot pose. The pipeline utilizes a custom virtual reality tool for labeling 3D scans that is used to train a semantic segmentation network. The masked point cloud is used to compute a trellis graph that identifies individual instances and extracts relevant features that are used by the SLAM module. We show that traditional lidar and image based methods fail in the forest environment on both Unmanned Aerial Vehicle (UAV) and hand-carry systems, while our method is more robust, scalable, and automatically generates tree diameter estimations.

The Design of Focal Plane Splitting Unit in a Hyperspectral Lidar System

A hyperspectral lidar system with focal plane splitting unit is proposed, including an off-axis receiving telescope, a grating spectrometer, and a single-tube detector array. The spectrum of the system covers 380-930nm, and is separated by grating spectrometer. The microlens-fiber coupling system guides the echo signal of 50 channels into each detector. The system solves the data processing problem of the bandwidth and gain the line array and area array detector in traditional hyperspectral lidar. And it also meets the requirement of the high efficiency splitting coupling and weak signal acquisition and detection.

Multi-Spectral Lidar: Radiometric Calibration, Canopy Spectral Reflectance, and Vegetation Vertical SVI Profiles

Multi-spectral (ms) airborne lidar data are enriched relative to traditional lidar due to the multiple channels of intensity digital numbers (DNs), which offer the potential for active Spectral Vegetation Indices (SVIs), enhanced classification, and change monitoring. However, in case of SVIs, indices should be calculated from spectral reflectance values derived from intensity DNs after calibration. In this paper, radiometric calibration of multi-spectral airborne lidar data is presented. A novel low-cost diffuse reflectance coating was adopted for creating radiometric targets. Comparability of spectral reflectance values derived from ms lidar data for coniferous stand (2.5% for 532 nm, 17.6% for 1064 nm, and 8.4% for 1550 nm) to available spectral libraries is shown. Active vertical profiles of SVIs were constructed and compared to modeled results available in the literature. The potential for a new landscape-level active 3D SVI voxel approach is demonstrated. Results of a field experiment with complex radiometric targets for estimating losses in detected lidar signals are described. Finally, an approach for estimating spectral reflectance values from lidar split returns is analyzed and the results show similarity of estimated values of spectral reflectance derived from split returns to spectral reflectance values obtained from single returns (p > 0.05 for paired test).

A 10-nm Spectral Resolution Hyperspectral LiDAR System Based on an Acousto-Optic Tunable Filter.

Hyperspectral LiDAR (HSL) technology can obtain spectral and ranging information from targets by processing the recorded waveforms and measuring the time of flight (ToF). With the development of the supercontinuum laser (SCL), it is technically easier to develop an active hyperspectral LiDAR system that can simultaneously collect both spatial information and extensive spectral information from targets. Compared with traditional LiDAR technology, which can only obtain range and intensity information at the selected spectral wavelengths, HSL detection technology has demonstrated its potential and adaptability for various quantitative applications from its spectrally resolved waveforms. However, with most previous HSLs, the collected spectral information is discrete, and such information might be insufficient and restrict the further applicability of the HSLs. In this paper, a tunable HSL technology using an acousto-optic tunable filter (AOTF) as a spectroscopic device was proposed, designed, and tested to address this issue. Both the general range precision and the accuracy of the spectral measurement were evaluated. By tuning the spectroscopic device in the time dimension, the proposed AOTF-HSL could achieve backscattered echo with continuous coverage of the full spectrum of 500–1000 nm, which had the unique characteristics of a continuous spectrum in the visible and near infrared (VNIR) regions with 10 nm spectral resolution. Yellow and green leaves from four plants (aloe, dracaena, balata, and radermachera) were measured using the AOTF-HSL to assess its feasibility in agriculture application. The spectral profiles measured by a standard spectrometer (SVC© HR-1024) were used as a reference for evaluating the measurements of the AOTF-HSL. The difference between the spectral measurements collected from active and passive instruments was minor. The comparison results show that the AOTF-based consecutive and high spectral resolution HSL was effective for this application.

Most similar neighbor imputation of forest attributes using metrics derived from combined airborne LIDAR and multispectral sensors

ABSTRACTIn the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.