Light Detection And Ranging System Research Articles

Improving the Accuracy of Digital Terrain Models Using Drone-Based LiDAR for the Morpho-Structural Analysis of Active Calderas: The Case of Ischia Island, Italy

Over the past two decades, the airborne Light Detection and Ranging (LiDAR) system has become a useful tool for acquiring high-resolution topographic data, especially in active tectonics studies. Analyzing Digital Terrain Models (DTMs) from LiDAR exposes morpho-structural elements, aiding in the understanding of fault zones, among other applications. Despite its effectiveness, challenges persist in regions with rapid deformation, dense vegetation, and human impact. We propose an adapted workflow transitioning from the conventional airborne LiDAR system to the usage of drone-based LiDAR technology for higher-resolution data acquisition. Additionally, drones offer a more cost-effective solution, both in an initial investment and ongoing operational expenses. Our goal is to demonstrate how drone-based LiDAR enhances the identification of active deformation features, particularly for earthquake-induced surface faulting. To evaluate the potential of our technique, we conducted a drone-based LiDAR survey in the Casamicciola Terme area, north of Ischia Island, Italy, known for the occurrence of destructive shallow earthquakes, including the 2017 Md = 4 event. We assessed the quality of our acquired DTM by comparing it with existing elevation datasets for the same area. We discuss the advantages and limitations of each DTM product in relation to our results, particularly when applied to fault mapping. By analyzing derivative DTM products, we identified the fault scarps within the Casamicciola Holocene Graben (CHG) and mapped its structural geometry in detail. The analysis of both linear and areal geomorphic features allowed us to identify the primary factors influencing the current morphological arrangement of the CHG area. Our detailed map depicts a nested graben formed by two main structures (the Maio and Sentinella faults) and minor internal faults (the Purgatorio and Nizzola faults). High-resolution DEMs acquired by drone-based LiDAR facilitated detailed studies of the geomorphology and fault activity. A similar approach can be applied in regions where the evidence of high slip-rate faults is difficult to identify due to vegetation cover and inaccessibility.

Time division multiplexing based multi-spectral semantic camera for LiDAR applications

The recent progress in the development of measurement systems for autonomous recognition had a substantial impact on emerging technology in numerous fields, especially robotics and automotive applications. In particular, time-of-flight (TOF) based light detection and ranging (LiDAR) systems enable to map the surrounding environmental information over long distances and with high accuracy. The combination of advanced LiDAR with an artificial intelligence platform allows enhanced object recognition and classification, which however still suffers from limitations of inaccuracy and misidentification. Recently, multi-spectral LiDAR systems have been employed to increase the object recognition performance by additionally providing material information in the short-wave infrared (SWIR) range where the reflection spectrum characteristics are typically very sensitive to material properties. However, previous multi-spectral LiDAR systems utilized band-pass filters or complex dispersive optical systems and even required multiple photodetectors, adding complexity and cost. In this work, we propose a time-division-multiplexing (TDM) based multi-spectral LiDAR system for semantic object inference by the simultaneous acquisition of spatial and spectral information. By utilizing the TDM method, we enable the simultaneous acquisition of spatial and spectral information as well as a TOF based distance map with minimized optical loss using only a single photodetector. Our LiDAR system utilizes nanosecond pulses of five different wavelengths in the SWIR range to acquire sufficient material information in addition to 3D spatial information. To demonstrate the recognition performance, we map the multi-spectral image from a human hand, a mannequin hand, a fabric gloved hand, a nitrile gloved hand, and a printed human hand onto an RGB-color encoded image, which clearly visualizes spectral differences as RGB color depending on the material while having a similar shape. Additionally, the classification performance of the multi-spectral image is demonstrated with a convolution neural network (CNN) model using the full multi-spectral data set. Our work presents a compact novel spectroscopic LiDAR system, which provides increased recognition performance and thus a great potential to improve safety and reliability in autonomous driving.

Silicon-based optical phased array with a reconfigurable aperture for “gaze” scanning of LiDAR

Light detection and ranging (LiDAR) serves as one of the key components in the fields of autonomous driving, surveying mapping, and environment detection. Conventionally, dense points clouds are pursued by LiDAR systems to provide high-definition 3D images. However, the LiDAR is typically used to produce abundant yet redundant data for scanning the homogeneous background of scenes, resulting in power waste and excessive processing time. Hence, it is highly desirable for a LiDAR system to “gaze” at the target of interest by dense scanning and rough sparse scans on the uninteresting areas. Here, we propose a LiDAR structure based on an optical phased array (OPA) with reconfigurable apertures to achieve such a gaze scanning function. By virtue of the cascaded optical switch integrated on the OPA chip, a 64-, 128-, 192-, or 256-channel antenna can be selected discretionarily to construct an aperture with variable size. The corresponding divergence angles for the far-field beam are 0.32°, 0.15°, 0.10°, and 0.08°, respectively. The reconfigurable-aperture OPA enables the LiDAR system to perform rough scans via the large beam spots prior to fine scans of the target by using the tiny beam spots. In this way, the OPA-based LiDAR can perform the “gaze” function and achieve full-range scanning efficiently. The scanning time and power consumption can be reduced by 1/4 while precise details of the target are maintained. Finally, we embed the OPA into a frequency-modulated continuous-wave (FMCW) system to demonstrate the “gaze” function in beam scanning. Experiment results show that the number of precise scanning points can be reduced by 2/3 yet can obtain the reasonable outline of the target. The reconfigurable-aperture OPA (RA-OPA) can be a promising candidate for the applications of rapid recognition, like car navigation and robot vision.

Airborne single-photon LiDAR towards a small-sized and low-power payload

Single-photon light detection and ranging (LiDAR) has played an important role in areas ranging from target identification and 3D imaging to remote sensing. Its high sensitivity provides the feasibility of lightweight LiDAR systems for the resource-limited airborne and spaceborne platforms. Here, we design and demonstrate an airborne single-photon LiDAR towards the compact, small-sized, and low-power payload. To reduce the system size, we utilize small telescopes with an optical aperture of 47 mm and develop the sub-pixel scanning approach to enhance the imaging resolution. With the fine scanning mirrors, we validate the super-resolution ability in the ground experiment by surpassing the system’s resolution by 2.5 times and achieve high-resolution 3D imaging in the airborne experiment. To realize low-power LiDAR, we employ photon-efficient computational algorithms and high-quality single-photon avalanche diode (SPAD) arrays. This enables us to reconstruct images from noisy data even under challenging conditions of two signal photons per pixel. Using the airborne single-photon LiDAR system, we demonstrate 3D imaging during daytime over a large area for remote sensing applications and show the capability to reveal the detailed features of various landforms and objects.

Wind Profile Reconstruction Based on Convolutional Neural Network for Incoherent Doppler Wind LiDAR

The rapid development of artificial intelligence (AI) and deep learning has revolutionized the field of data analysis in recent years, including signal data acquired by remote sensors. Light Detection and Ranging (LiDAR) technology is widely used in atmospheric research for measuring various atmospheric parameters. Wind measurement using LiDAR data has traditionally relied on the spectral centroid (SC) algorithm. However, this approach has limitations in handling LiDAR data, particularly in low signal-to-noise ratio (SNR) regions. To overcome these limitations, this study leverages the capabilities of customized deep-learning techniques to achieve accurate wind profile reconstruction. The study uses datasets obtained from the European Centre for Medium Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) and the mobile Incoherent Doppler LiDAR (ICDL) system constructed by the University of Science and Technology of China. We present a simulation-based approach for generating wind profiles from the statistical data and the associated theoretical calculations. Whereafter, our team constructed a convolutional neural network (CNN) model based on the U-Net architecture to replace the SC algorithm for LiDAR data post-processing. The CNN-generated results are evaluated and compared with the SC results and the ERA5 data. This study highlights the potential of deep learning-based techniques in atmospheric research and their ability to provide more accurate and reliable results.

Automatic multi-LiDAR calibration and refinement method

Sensor-based environmental perception is a crucial component of autonomous driving systems. To perceive the surrounding environment better, an intelligent system would utilize multiple LiDARs (3D Light Detection and Ranging). The accuracy of the perception largely depends on the quality of the sensor calibration. This research aims to develop a robust, fast, automatic, and accurate calibration strategy for multiple LiDAR systems. Our proposed multi-LiDAR calibration method consists of two stages: rough and refinement calibration. In the first stage, sensors are roughly calibrated from an arbitrary initial position using a deep neural network that does not rely on prior information or constraints on the initial sensor pose. In the second stage, we propose the octree-based refinement, an optimization method that considers sensor noise and prioritization. Our strategy is robust, fast, and not restricted to any environment. Additionally, we collected two datasets consisting of both real-world and simulated scenarios. Our experimental results from both datasets demonstrate the reliability and accuracy of our method. All the related datasets and codes are open-sourced on the GitHub website at https://github.com/OpenCalib/LiDAR2LiDAR.

LTA‐OM: Long‐term association LiDAR–IMU odometry and mapping

AbstractThis paper focuses on the Light Detection and Ranging (LiDAR)–Inertial Measurement Unit (IMU) simultaneous localization and mapping (SLAM) problem: How to fuse the sensor measurement from the LiDAR and IMU to online estimate robot's poses and build a consistent map of the environment. This paper presents LTA‐OM: an efficient, robust, and accurate LiDAR SLAM system. Employing fast direct LiDAR‐inertial odometry (FAST‐LIO2) and Stable Triangle Descriptor as LiDAR–IMU odometry and the loop detection method, respectively, LTA‐OM is implemented to be functionally complete, including loop detection and correction, false‐positive loop closure rejection, long‐term association (LTA) mapping, and multisession localization and mapping. One novelty of this paper is the real‐time LTA mapping, which exploits the direct scan‐to‐map registration of FAST‐LIO2 and employs the corrected history map to provide direct global constraints to the LIO mapping process. LTA mapping also has the notable advantage of achieving drift‐free odometry at revisit places. Besides, a multisession mode is designed to allow the user to store the current session's results, including the corrected map points, optimized odometry, and descriptor database for future sessions. The benefits of this mode are additional accuracy improvement and consistent map stitching, which is helpful for life‐long mapping. Furthermore, LTA‐OM has valuable features for robot control and path planning, including high‐frequency and real‐time odometry, driftless odometry at revisit places, and fast loop closing convergence. LTA‐OM is versatile as it is applicable to both multiline spinning and solid‐state LiDARs, mobile robots and handheld platforms. In experiments, we exhaustively benchmark LTA‐OM and other state‐of‐the‐art LiDAR systems with 18 data sequences. The results show that LTA‐OM steadily outperforms other systems regarding trajectory accuracy, map consistency, and time consumption. The robustness of LTA‐OM is validated in a challenging scene—a multilevel building having similar structures at different levels. To demonstrate our system, we created a video which can be found on https://youtu.be/DVwppEKlKps.

Multispectral Light Detection and Ranging Technology and Applications: A Review.

Light Detection and Ranging (LiDAR) is a well-established active technology for the direct acquisition of 3D data. In recent years, the geometric information collected by LiDAR sensors has been widely combined with optical images to provide supplementary spectral information to achieve more precise results in diverse remote sensing applications. The emergence of active Multispectral LiDAR (MSL) systems, which operate on different wavelengths, has recently been revolutionizing the simultaneous acquisition of height and intensity information. So far, MSL technology has been successfully applied for fine-scale mapping in various domains. However, a comprehensive review of this modern technology is currently lacking. Hence, this study presents an exhaustive overview of the current state-of-the-art in MSL systems by reviewing the latest technologies for MSL data acquisition. Moreover, the paper reports an in-depth analysis of the diverse applications of MSL, spanning across fields of "ecology and forestry", "objects and Land Use Land Cover (LULC) classification", "change detection", "bathymetry", "topographic mapping", "archaeology and geology", and "navigation". Our systematic review uncovers the potentials, opportunities, and challenges of the recently emerged MSL systems, which integrate spatial-spectral data and unlock the capability for precise multi-dimensional (nD) mapping using only a single-data source.

Simplified correlation-domain LiDAR without long delay fiber

Light detection and ranging (LiDAR) measures distance by emitting light onto targets and analyzing reflections. In addition to conventional LiDAR systems based on time-of-flight and frequency-modulated continuous-wave methods, correlation-domain LiDAR has been developed but with some challenges in miniaturization due to the use of a long delay fiber. This study addresses this issue by specifying modulation conditions and removing the delay fiber. We then demonstrate the distance measurement by this simplified correlation-domain LiDAR. This result enhances the potential of integrating correlation-domain LiDAR into various applications.

Breaking ground in Ghana: LiDAR for site monitoring and its implications on project delivery

Light Detection and Ranging (LiDAR) has in contemporary times become a popular technology in the developed world, being utilized in city planning and infrastructure development. Documentation on the knowledge of its usage in Ghana is scanty. A major challenge that hinders the effective and timely delivery of infrastructural projects in Ghana is the delay in validating certificates for payment as a result of the use of traditional methods of measurement. This paper analysed the use of LiDAR in Aboragyei dumpsite in comparison with the existing traditional method of measurement, which is both time-consuming and prone to measurement inaccuracies. The objective was to evaluate the level of accuracy in measurement between the two methods. This paper used quasi-experimental designs on LiDAR technology to capture 3D models and extract vertical and horizontal measurements from them within a short period. The findings from the comparative assessment indicated that LiDAR technology speeds up infrastructure projects by enhancing not only procedure productivity but also cross-team communication. The cloud point models generated by LiDAR mapping do not deform when confronted with angular or complex geometry, unlike theodolite. This study makes a strong case for the utilization of LiDAR as a method for measuring vertical and horizontal angles by illuminating the target with laser light and measuring each reflection with a sensor. LiDAR system if adapted for site monitoring in Ghana will help avoid distortions in measuring horizontal and vertical angles and save time. However, a major constraint using it in Ghana was the unstable weather patterns.

Interpolation linearization predistortion technology for FMCW LiDAR.

Frequency-modulated continuous-wave (FMCW) laser ranging technology is an important development direction of light detection and ranging (LiDAR) for the future. It has the advantages of high ranging accuracy, high resolution, wide range, and no ranging blind zone. A distributed feedback laser can be used as a high-quality light source in FMCW laser ranging systems because of its wide frequency modulation range, simple frequency modulation mode, and small package. Aiming at the nonlinear problem of the laser in the frequency modulation process, we present a novel, to our knowledge, predistortion algorithm based on interpolation linear fitting to enhance the linearity of the FMCW laser for LiDAR systems. The sweeping frequency curve of the laser is obtained using the Hilbert transform, and then the sweeping frequency curve is segmented and linearly fitted to calculate the interpolated driving current signals corresponding to linear frequency changes. Using this method, we achieved a nonlinearity error lower than 1e-7 for the swept-frequency signal and demonstrated that the ranging error is less than ±5c m at a distance of 100m in the FMCW system. In addition, we also demonstrated a 3D static object point cloud with high imaging quality. Compared with the iterative predistortion algorithm based on the function fitting, this method avoids fitting errors at the inflection points of the triangular swept-frequency signal and the complexity of multiple iterative calculations. It enables rapid generation of pre-distorted swept-frequency signals, making it particularly suitable for real-time applications of automotive LiDAR systems.

Spatio-spectral 4D coherent ranging using a flutter-wavelength-swept laser

Coherent light detection and ranging (LiDAR), particularly the frequency-modulated continuous-wave LiDAR, is a robust optical imaging technology for measuring long-range distance and velocity in three dimensions (3D). We propose a spatio-spectral coherent LiDAR based on a unique wavelength-swept laser to enable both axial coherent ranging and lateral spatio-spectral beam scanning simultaneously. Instead of the conventional unidirectional wavelength-swept laser, a flutter-wavelength-swept laser (FWSL) successfully decoupled bidirectional wavelength modulation and continuous wavelength sweep, which overcame the measurable distance limited by the sampling process. The decoupled operation in FWSL enabled sequential sampling of flutter-wavelength modulation across its wide spectral bandwidth of 160 nm and, thus, allowed simultaneous distance and velocity measurement over an extended measurable distance. Herein, complete four-dimensional (4D) imaging, combining real-time 3D distance and velocity measurements, was implemented by solid-state beam scanning. An acousto-optic scanner was synchronized to facilitate the other lateral beam scanning, resulting in an optimized solid-state coherent LiDAR system. The proposed spatio-spectral coherent LiDAR system achieved high-resolution coherent ranging over long distances and real-time 4D imaging with a frame rate of 10 Hz, even in challenging environments.

Enhancing LiDAR performance using threshold photon-number-resolving detection.

Single-photon light detection and ranging (LiDAR) is widely used to reconstruct 3D scenes. Nevertheless, depth and reflectivity maps obtained by single-photon detection usually suffer from noise problems. Threshold LiDAR techniques using photon-number-resolving detectors were proposed to suppress noise by filtering low photon numbers, but these techniques renounce multiple levels of information and could not be compatible when it comes to high-noise low-signal regime. In this manuscript, we propose a detection scheme which combines the noise suppression of threshold detection with the signal amplification of photon-number-resolving detectors to further enhance LiDAR performance. The enhancement attained is compared to single-photon and threshold detection schemes under a wide range of signal and noise conditions, in terms of signal-to-noise-ratio (SNR), detection rate and false alarm rate, which are key metrics for LiDAR. Extensive simulations and real-world experiments show that the proposed scheme can reconstruct better depth and reflectivity maps. These results enable the development of high-efficient and low-noise LiDAR systems.

FMCW LiDAR with a coherent receiver chip based on 3 μm SOI photonics platform

Silicon photonics is considered as the ideal solution for chip-scale solid Frequency Modulated Continuous Wave (FMCW) Light Detection and Ranging (LiDAR). In this paper, a coherent receiver chip, integrating beam splitters, 180-degree mixers, germanium-balanced photodetectors, and an on-chip temperature sensor is presented. Benefiting from the micron-scale silicon photonics platform, the propagation loss of the waveguide is 0.19 dB/cm, and the imbalance of 180-degree mixers is only 0.30 dB at 1550 nm. The 3 dB cut-off frequency of the balanced photodetector reaches 37.3 GHz, and the responsivity is 0.32A considering the coupling loss. An FMCW LiDAR system with the coherent receiver chip is built for ranging and velocity measurement. Ranging of distance at 120 m with the precision of 0.3 m is achieved. The micron-scale silicon photonics platform can also provide the capabilities for multi-channel coherent receivers and monolithic integration of transmitters and receivers, providing another practical approach for solid state FMCW LiDAR system which can be applied to autopilot, medical and other field for their lightweight requirement.

High Resolution 3D Model of Heritage Landscapes Using UAS LiDAR: The Tajos de Alhama de Granada, Spain

The Tajos de Alhama de Granada, which since ancient times have inspired and surprised locals and strangers, especially foreign travelers, constituted a unique landscape, cultural and ethnological heritage of Spain, linked to water and its old flour mills. And, they are currently at serious risk of degradation. The aim of this research is to obtain a high-resolution 3D model capable of documenting this historical heritage environment with a high level of detail, using a methodology that includes small light weight LiDAR (Light Detection and Ranging) system for UAS (Unmanned Aircraft System). The model obtained should serve, on the one hand, as a valuable tool for knowledge and analysis of all the elements (river, lake, ditches, dams, mills, aqueducts, and paths) that made up this place, registered as a picturesque landscape for its extraordinary beauty and uniqueness, and on the other hand, as a basis for the development of rehabilitation and architectural restoration projects that would have to be undertaken to preserve this cultural and landscape legacy.

Drone Multiline Light Detection and Ranging Data Filtering in Coastal Salt Marshes Using Extreme Gradient Boosting Model

Quantitatively characterizing coastal salt-marsh terrains and the corresponding spatiotemporal changes are crucial for formulating comprehensive management plans and clarifying the dynamic carbon evolution. Multiline light detection and ranging (LiDAR) exhibits great capability for terrain measuring for salt marshes with strong penetration performance and a new scanning mode. The prerequisite to obtaining the high-precision terrain requires accurate filtering of the salt-marsh vegetation points from the ground/mudflat ones in the multiline LiDAR data. In this study, a new alternative salt-marsh vegetation point-cloud filtering method is proposed for drone multiline LiDAR based on the extreme gradient boosting (i.e., XGBoost) model. According to the basic principle that vegetation and the ground exhibit different geometric and radiometric characteristics, the XGBoost is constructed to model the relationships of point categories with a series of selected basic geometric and radiometric metrics (i.e., distance, scan angle, elevation, normal vectors, and intensity), where absent instantaneous scan geometry (i.e., distance and scan angle) for each point is accurately estimated according to the scanning principles and point-cloud spatial distribution characteristics of drone multiline LiDAR. Based on the constructed model, the combination of the selected features can accurately and intelligently predict the category of each point. The proposed method is tested in a coastal salt marsh in Shanghai, China by a drone 16-line LiDAR system. The results demonstrate that the averaged AUC and G-mean values of the proposed method are 0.9111 and 0.9063, respectively. The proposed method exhibits enhanced applicability and versatility and outperforms the traditional and other machine-learning methods in different areas with varying topography and vegetation-growth status, which shows promising potential for point-cloud filtering and classification, particularly in extreme environments where the terrains, land covers, and point-cloud distributions are highly complicated.

Extended field-of-view scanning in LiDAR systems using computer-generated holography-based spatial light modulator

A method is proposed for realizing extended field-of-view (FOV) scanning in light detection and ranging (LiDAR) systems utilizing computer-generated holography, a spatial light modulator (SLM), and an eye piece. In the proposed approach, an SLM driven by appropriately-designed computer-generated holograms (CGHs) is used to collimate the incoming rays and produce a large field of view (FOV). Five FOVs are considered for illustration purposes: 0°, -5°, -10°, -15°, and -20° For each FOV, the necessary CGH is determined by optimizing the “Binary 1″ surface in Zemax simulations and then processing the resulting phase by a modulo-2π operation. The feasibility of the proposed method is demonstrated using an experimental telescope system. The results show that the FOVs generated by the system deviate by no more than 0.566° from the target values. Moreover, the focus spot maintains a size of approximately 3 mm at two different observation planes located at 73 cm and 198 cm from the eyepiece, respectively. In other words, the system not only provides an extended FOV scanning range, but also delivers an excellent collimation performance. Overall, the results thus confirm the potential of the proposed method for large FOV scanning in practical LiDAR systems.

EVALUATION OF CONSUMER-GRADE AND SURVEY-GRADE UAV-LIDAR

Abstract. Driven by developments in the automotive industry, the availability of compact consumer-grade LiDAR (Light Detection and Ranging) sensors has increased significantly in recent years. Some of these sensors are also suitable for UAV-based surveying tasks. This paper first discusses the differences between consumer-grade and survey-grade LiDAR systems. Special attention will be paid to the scanning mechanisms used on the one hand and to different solutions for the transceiver units on the other hand. Based on the technical data of two concrete systems, the consumer-grade DJI Zenmuse L1 sensor and the survey-grade scanner RIEGL VUX-1UAV, the expected effects of the sensor parameters on the 3D point cloud are first discussed theoretically and then verified using an exemplary data set in Hessigheim (Baden-Württemberg, Germany). The analysis shows the possibilities and limitations of consumer-grade LiDAR. Compared to the low-cost sensor, the high-end scanner exhibits lower range measurement noise (5–10 mm) and better 3D point location accuracy. Furthermore, the higher laser beam quality of high-end devices (beam divergence, beam shape) enables more detailed object detection at the same point density. With moderate accuracy requirements of 5–10 cm, however, applications in the geodetic-cartographic context also arise for the considerably less expensive consumer-grade LiDAR systems.

Cascaded 2D Micromirror with Application to LiDAR.

This paper introduced a novel approach to enhance the vertical scanning angle of a large aperture 2D electromagnetic micromirror through the utilization of a cascaded torsional beam design. The primary objective was to increase the vertical scanning angle without compromising the robustness, which was achieved by optimizing the trade-off between the rotation angle and the first mode of resonant frequency. The cascaded design provides flexibility to either increase the outer frame's rotation angle without sacrificing torsional stiffness or enhance the torsion beam's stiffness while maintaining the same rotation angle, thus elevating the first-mode resonant frequency and overall robustness. The effectiveness of the cascaded design was demonstrated through a comparative study with a non-cascaded 2D micromirror possessing the same aperture size, torque, and mass moment of inertia. Theoretical analysis and finite-element simulation are employed to determine critical parameters such as the stiffness ratio between the cascaded torsion beams, and to predict improvements in the scanning angle and primary resonant frequency brought by the cascaded design. Prototypes of both cascaded and non-cascaded designs are fabricated using a flexible printed circuit board combined with Computer numerical control (CNC) machining of a Ti-alloy thin film, confirming the superior performance of the cascaded 2D micromirror. The cascaded design achieved vertical scanning angles up to 26% higher than the traditional design when both were actuated at close resonance frequencies. Additionally, the micromirror was successfully integrated into a 3D LiDAR system. The light detection and ranging (LiDAR) system was modelled in Zemax OpticStudio to find the optimized design and assembly positions.

Multispectral LiDAR-based underwater ore classification using a tunable laser source

Light Detection and Ranging (LiDAR) technology has been extensively used to collect precise geometric information. However, conventional LiDAR sensors are restricted to single-wavelength operation, limiting their ability to capture valuable spectral information. Multispectral LiDAR (MSL) systems, on the other hand, have emerged as a promising solution by facilitating the active acquisition of spatial and spectral data. These advanced systems exhibit tremendous potential in facilitating advanced analysis and classification of seafloor sediments. In this study, the first demonstration of an MSL system optimized for investigating the feasibility of underwater ore classification using a tunable laser source is described. The MSL prototype features a spectral resolution of 10 nm, and 11 spectral channels, covering the range from 460 to 560 nm. Laboratory-based experiments were conducted to evaluate the accuracy of range measurements and the classification performance of the system. The spectral profiles of eight distinct ore samples acquired by the MSL were utilized for classification using four classification methods: naive Bayes (NB), k Nearest Neighbor (KNN), support vector machine (SVM), and random forest (RF). Furthermore, comparative analyses were conducted to investigate the classification enhancements realized by leveraging the MSL system with multiple spectral channels as opposed to single-wavelength and dual-wavelength systems.