Light Detection And Ranging System Research Articles

Stockpile Volume Estimation in Open and Confined Environments: A Review

This paper offers a comprehensive review of traditional and advanced stockpile volume-estimation techniques employed within both outdoor and indoor confined spaces, whether that be a terrestrial- or an aerial-based technique. Traditional methods, such as manual measurement and satellite imagery, exhibit limitations in handling irregular or constantly changing stockpiles. On the other hand, more advanced techniques, such as global navigation satellite system (GNSS), terrestrial laser scanning (TLS), drone photogrammetry, and airborne light detection and ranging (LiDAR), have emerged to address these challenges, providing enhanced accuracy and efficiency. Terrestrial techniques relying on GNSS, TLS, and LiDAR offer accurate solutions; however, to minimize or eliminate occlusions, surveyors must access geometrically constrained places, representing a serious safety hazard. With the speedy rise of drone technologies, it was not unexpected that they found their way to the stockpile volume-estimation application, offering advantages such as ease of use, speed, safety, occlusion elimination, and acceptable accuracy compared to current standard methods, such as TLS and GNSS. For outdoor drone missions, image-based approaches, like drone photogrammetry, surpass airborne LiDAR in cost-effectiveness, ease of deployment, and color information, whereas airborne LiDAR becomes advantageous when mapping complex terrain with vegetation cover, mapping during low-light or dusty conditions, and/or detecting small or narrow objects. Indoor missions, on the other hand, face challenges such as low lighting, obstacles, dust, and limited space. For such applications, most studies applied LiDAR sensors mounted on tripods or integrated on rail platforms, whereas very few utilized drone solutions. In fact, the choice of the most suitable technique/approach depends on factors such as site complexity, required accuracy, project cost, and safety considerations. However, this review puts more focus on the potential of drones for stockpile volume estimation in confined spaces, and explores emerging technologies, such as solid-state LiDAR and indoor localization systems, which hold significant promise for the future. Notably, further research and real-world applications of these technologies will be essential for realizing their full potential and overcoming the challenges of operating robots in confined spaces.

A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors.

In this work, we introduce a novel approach to model the rain and fog effect on the light detection and ranging (LiDAR) sensor performance for the simulation-based testing of LiDAR systems. The proposed methodology allows for the simulation of the rain and fog effect using the rigorous applications of the Mie scattering theory on the time domain for transient and point cloud levels for spatial analyses. The time domain analysis permits us to benchmark the virtual LiDAR signal attenuation and signal-to-noise ratio (SNR) caused by rain and fog droplets. In addition, the detection rate (DR), false detection rate (FDR), and distance error derror of the virtual LiDAR sensor due to rain and fog droplets are evaluated on the point cloud level. The mean absolute percentage error (MAPE) is used to quantify the simulation and real measurement results on the time domain and point cloud levels for the rain and fog droplets. The results of the simulation and real measurements match well on the time domain and point cloud levels if the simulated and real rain distributions are the same. The real and virtual LiDAR sensor performance degrades more under the influence of fog droplets than in rain.

High precision 3D imaging with timing corrected single photon LiDAR.

Single photon light detection and ranging (LiDAR) is an important technique for high precision long distance three-dimensional (3D) imaging. However, due to the effects and native limitations of system components, there exists ranging errors when using LiDAR system. For the LiDAR system that requires trigger detector to provide synchronization signals, the fluctuation of laser pulse energy causes the change of the initial time of the constant threshold triggered timing module, and subsequently leads to the ranging error. In this paper, we build a dual SPADs LiDAR system to avoid the ranging error caused by the fluctuation of laser pulse energy. By adding a reference optical path, the flight time of signal photons is corrected by reference photons, so as to realize the correction of ranging. A series of experiments demonstrate that the proposed LiDAR system has the capability of high precision ranging and 3D imaging. The system achieves range of error of 0.15 mm and range resolution of 0.3 mm at a distance of 29 m.

A 4–40 V Wide Input Range Boost Converter With the Protection Re-Cycling Technique for 200 W High Power LiDAR System in a Long-Distance Object Detection

This article presents a high conversion ratio (CR) boost converter with the protection re-cycling (PRC) technique to prevent the eGaN power FET from being damaged. The PRC technique mainly redirects the current to the storage capacitor. When the eGaN power FET is turned off, not only the eGaN power FET will not be damaged by the high L d <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$i$</tex-math> </inline-formula> /d t, but also the redirected current can be used to improve efficiency. For automotive applications, the battery voltage may crank to a low voltage of 4 V in the case of sudden power-on, and immediately rise to a high voltage of 40 V during a loading pump. Therefore, this article also proposed a wide input range (WIR) boost converter to ensure the correct operation of the light detection and ranging (LiDAR) system in any input voltage conditions.

Evaluation of wind measurement methods for determination of realistic wind shear: A case study in Aksaray, Turkey

Wind energy has become a large and important market in Turkey. Wind resource assessment and hub height extrapolation are the most analyses during wind power plant development. Wind shear is a critical parameter for wind speed analyses in wind energy power plant projects. The study covered measurement analyses of LIDAR (Light Detection and Ranging) and wind measurement mast system data that were compared as common in literature. Then wind shear formation and analysis factors were realistically evaluated with the multi-criteria method first in the literature using wind speed, wind direction, WSC, roughness, turbulence intensity (TI) and correlation factor. Under 100 m measurements were extrapolated to 101 m and upper 101 m. The predicted 245 pieces of the data set by using power law and log law and the measured data set were correlated with an anemometer and the LIDAR measurements to figure out an efficient prediction method. 20 pieces of wind shear coefficients (WSCs) were calculated using mast-mast, LIDAR-LIDAR, mast-LIDAR and LIDAR-mast wind speed measurement. Then, these WSCs were applied and tested for various measured wind speeds and the instantaneous wind speed was predicted with WSC values. Simultaneously, the log law method was repeated for different roughness values and the ideal roughness value was calculated for the site specifications. These predictions provided specific information about the height of wind shear and TI height supported by LIDAR and anemometers. In comparing data estimated by the power-law and log-law methods with measurements, good and acceptable results were obtained with more than a 0.999 correlation coefficient.

Frequency-angular resolving LiDAR using chip-scale acousto-optic beam steering.

Thanks to its superior imaging resolution and range, light detection and ranging (LiDAR) is fast becoming an indispensable optical perception technology for intelligent automation systems including autonomous vehicles and robotics1-3. The development of next-generation LiDAR systems critically needs a non-mechanical beam-steering system that scans the laser beam in space. Various beam-steering technologies4 have been developed, including optical phased array5-8, spatial light modulation9-11, focal plane switch array12,13, dispersive frequency comb14,15 and spectro-temporal modulation16. However, many of these systems continue to be bulky, fragile and expensive. Here we report an on-chip, acousto-optic beam-steering technique that uses only a single gigahertz acoustic transducer to steer light beams into free space. Exploiting the physics of Brillouin scattering17,18, in which beams steered at different angles are labelled with unique frequency shifts, this technique uses a single coherent receiver to resolve the angular position of an object in the frequency domain, and enables frequency-angular resolving LiDAR. We demonstrate a simple device construction, control system for beam steering and frequency domain detection scheme. The system achieves frequency-modulated continuous-wave ranging with an 18° field of view, 0.12° angular resolution and a ranging distance up to115 m. The demonstration can be scaled up to an array realizing miniature, low-cost frequency-angular resolving LiDAR imaging systems with a wide two-dimensional field of view. This development represents a step towards the widespread use of LiDAR in automation, navigation and robotics.

Biaxial Piezoelectric MEMS Mirrors with Low Absorption Coating for 1550 nm Long-Range LIDAR.

This paper presents the fabrication and characterization of a biaxial MEMS (MicroElectroMechanical System) scanner based on PZT (Lead Zirconate Titanate) which incorporates a low-absorption dielectric multilayer coating, i.e., a Bragg reflector. These 2 mm square MEMS mirrors, developed on 8-inch silicon wafers using VLSI (Very Large Scale Integration) technology are intended for long-range (>100 m) LIDAR (LIght Detection And Ranging) applications using a 2 W (average power) pulsed laser at 1550 nm. For this laser power, the use of a standard metal reflector leads to damaging overheating. To solve this problem, we have developed and optimised a physical sputtering (PVD) Bragg reflector deposition process compatible with our sol-gel piezoelectric motor. Experimental absorption measurements, performed at 1550 nm and show up to 24 times lower incident power absorption than the best metallic reflective coating (Au). Furthermore, we validated that the characteristics of the PZT, as well as the performance of the Bragg mirrors in terms of optical scanning angles, were identical to those of the Au reflector. These results open up the possibility of increasing the laser power beyond 2W for LIDAR applications or other applications requiring high optical power. Finally, a packaged 2D scanner was integrated into a LIDAR system and three-dimensional point cloud images were obtained, demonstrating the scanning stability and operability of these 2D MEMS mirrors.

A 40nm 2TOPS/W Depth-Completion Neural Network Accelerator SoC With Efficient Depth Engine for Realtime LiDAR Systems

Light Detection and Ranging (LiDAR) is becoming a critical requirement for future computer vision applications, such as AR/VR (iPhone-LiDAR) and ADAS (Automotive-LiDAR). A depth point-cloud input has different characteristics than a conventional RGB image input, such that the CNN depthinference implementation is unique when compared with a standard super-resolution CNN(SR-CNN). In this brief, we present a heterogeneous AI-accelerator SoC, which is specific to depth image completion computation. Three key innovations are introduced to improve SoC’s performance. First, to accommodate the unique input data structure of a depth input, a fully-filled dataflow management engine is proposed to pre-process the RGB+Depth input, significantly improving processing element utilization (PEU). Second, to improve the efficiency of the instruction configurations of the CNN accelerator, a hardware-tiling co-processor is proposed that performs the tiling strategy of the CNN accelerator, assigning each sub-job to the PE array directly, therefore reducing the time for task assignments. Third, due to the large number of vector operations required for the postprocess in the neural network, a RISC-V core is incorporated to execute vector computations better. The SoC is implemented in 40nm CMOS process, achieving 2TOPs/W energy efficiency with 34fps throughput under VGA-resolution output for realtime LiDAR systems.

Long range 3D imaging through atmospheric obscurants using array-based single-photon LiDAR.

Single-photon light detection and ranging (LiDAR) has emerged as a strong candidate technology for active imaging applications. In particular, the single-photon sensitivity and picosecond timing resolution permits high-precision three-dimensional (3D) imaging capability through atmospheric obscurants including fog, haze and smoke. Here we demonstrate an array-based single-photon LiDAR system, which is capable of performing 3D imaging in atmospheric obscurant over long ranges. By adopting the optical optimization of system and the photon-efficient imaging algorithm, we acquire depth and intensity images through dense fog equivalent to 2.74 attenuation lengths at distances of 13.4 km and 20.0 km. Furthermore, we demonstrate real-time 3D imaging for moving targets at 20 frames per second in mist weather conditions over 10.5 km. The results indicate great potential for practical applications of vehicle navigation and target recognition in challenging weather.

Phase-Modulated Continuous-Wave Coherent Ranging Method and Anti-Interference Evaluation

Light detection and ranging (LiDAR) has been applied in many areas because of its excellent performance. An easily achievable, cost-effective, and high-performance ranging method is a major challenge of LiDAR. Meanwhile, with the increasing applications of LiDAR, numerous LiDARs can be made to operate simultaneously, and potential interference is inevitable. Therefore, immunity against interference is paramount in LiDAR systems. In this paper, we demonstrated a ranging method referred to as phase-modulated continuous-wave (PhMCW). A detection range of 50 m and a ranging error of 2.2 cm are achieved. A one-dimensional scanning LiDAR system that is capable of detecting targets at 28 m is built, demonstrating the validation of the PhMCW method. Moreover, we propose a quantitative method for evaluating the anti-interference capability of lidar systems. The p-values of the Ljung–Box test were 0.0589 and 0.6327 for ToF and coherent LiDAR interferences, respectively, indicating that the PhMCW system is immune to interference. The proposed method can be applied to all types of LiDAR systems, regardless of the ranging method or beam-steering technique used.

An exploration, analysis, and correction of the distance effect on terrestrial hyperspectral LiDAR data

Terrestrial hyperspectral light detection and ranging (LiDAR) (HSL), as one novel LiDAR system integrated with more wavelengths, has supplied enhanced possibilities in many fields by delivering the geographic coordinate and the hyperspectral backscattered intensity simultaneously. However, like the traditional single-wavelength LiDAR, it is faced with the distance effect, which restricts the accurate acquisition of the hyperspectral intensities and the quantitative retrieval of characteristics of the targets. The previously reported distance effect correction models mainly concentrated on the distance effect correction for the single-wavelength LiDAR system, and some normalization methods with the standard reflectance panel were also provided to calibrate the data measured by multispectral LiDAR and HSL, producing the reflectance-related value. Nevertheless, it is valuable to systematically explore the distance effect of HSL over long distances and search for the corresponding correction methods. Along these lines, in this study, the homogeneous targets and standard reflectance panels with different reflectance values were employed to participate in the distance experiment to systematically explore and analyze the distance effect phenomenon on terrestrial HSL data. A novel practical piecewise fitting model was proposed to output realistic correction results and it was also compared with the classic inverse distance square law and polynomial fitting model. To further demonstrate the feasibility and good capability, the models were employed in the intensity correction for the various vegetation leaf species. A simple 3-D scene experiment was then carried out to demonstrate the intensity variation before and after the implementation of the distance effect correction. From the acquired result, it suggests that (1) the distance effect on terrestrial HSL data is not related to the wavelength and the scanned target. All the wavelengths share a common distance effect; (2) the concept of the ‘distance effect function’ could represent well the distance effect for the terrestrial HSL; (3) as far as the correction ability is concerned, the proposed piecewise fitting model achieved better performance than the others because it exhibited the smallest standard deviation (σ), coefficient of variation (CV), and the ratio of the coefficient of variation (ε) for the correction results of the homogeneous targets, and the leaf experiments confirmed it again; and (4) the further 3-D experiment result exhibited the intensity variation. It proves that the distance effect does exist in the 3-D scanning results and the correction is necessary. Besides, for small-scanned scenes, the incidence angle effect dominates and largely affects the intensity data instead of the distance effect. This study enriches the study of the distance effect mechanism on the terrestrial HSL data, which facilitates the development of research on the radiative effect correction of the HSL. At the same time, the proposed model can be adopted for the analysis and correction of the distance effect on terrestrial HSL data in actual scanning tasks.

Memory Conscious Machine Learning Method to Extract Time-of-Flight Data From Flash LiDARs

This letter presents a memory-conscious architecture for single-photon-avalanche-diode (SPAD) light detection and ranging (LiDAR) systems to extract time-of-flight (ToF) information by learning the distribution of incoming photons' ToF to reduce the I/O bandwidth required by flash LiDAR digitizers. Such LiDAR systems can be sensitive to single photons, which makes them an excellent choice for both short- and long-range applications. However, counting and processing every photon means that the sensor front-end generates a massive amount of data to be processed in each frame. The proposed architecture will tackle this challenge by learning the incoming photons' distribution on-chip using mini-batches of time-stamped data from time-to-digital converters. Hence, only learned parameters need to be transferred off-chip instead of the raw data to re-create the histogram. We demonstrate the feasibility of this model in a Monte Carlo simulation.

Solid-State dToF LiDAR System Using an Eight-Channel Addressable, 20-W/Ch Transmitter, and a 128 × 128 SPAD Receiver With SNR-Based Pixel Binning and Resolution Upscaling

An entire solid-state direct time-of-flight (dToF) light detection and ranging (LiDAR) system that incorporates innovations for both the transmitter (TX) and the receiver (RX) is presented in this work. For the illumination TX, we demonstrate solid-state channel addressability, which significantly reduces the transmit power and improves the ranging distance by dividing the field of view (FoV) into separately illuminated sub-regions. In the RX, we introduce single-photon avalanche diodes (SPADs) pixel binning, which enables reconfigurability of the sensor’s spatial resolution. Finally, we introduce a machine learning (ML) technique that enables this pixel-binned depth sensor to upscale its spatial resolution after training/inference fusion with the intensity image. The laser diode driver (LDD) chip is implemented in the 180-nm bipolar-CMOS-DMOS (BCD) process and is capable of pumping more than 8-A peak current into a multi-junction vertical-cavity surface-emitting laser (VCSEL) array, producing up to 20.3-W optical pulses under 12.5-V supply voltage. The sensor chip is also implemented in the 180-nm BCD process with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$128 \times 128$ </tex-math></inline-formula> SPAD array and reconfigurable pixel binning. Hardware and software co-optimization under low signal-to-noise ratio (SNR) conditions with ML-based spatial resolution upscaling is demonstrated.

ICESat-2 data classification and estimation of terrain height and canopy height

ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) was launched in 2018 with a photon-counting LiDAR (Light Detection and Ranging) system, ATLAS (Advanced Topographic Laser Altimeter System). It is collecting massive earth elevation data all over the world, which has shown the potential of large-scale forest monitoring. However, the energy emitted by the LiDAR system is low, and the received signals are easily affected by noise. Accurate classification of photons is an important step for forest parameter retrieval. Given the limitations of existing photon classification algorithms in areas with complex terrain, we proposed an improved local outlier factor algorithm with a rotating search area (LOFR). First, photons are transformed to the along-track direction, and noise photons are preliminarily filtered out by using the elevation histogram and elevation statistical methods. Next, ground photons are extracted by using the LOF (Local Outlier Factor) with the horizontal ellipse search area algorithm (LOFE) during an initial classification stage to filter photons that are far away from the ground. During the refined classification stage, which is the core of the algorithm, the terrain slope is calculated according to the ground photons extracted during initial classification. The elliptic search area is then rotated to align its long axis with the terrain slope. Finally, the LOFR scores of the photons are calculated to remove noise photons and signal photons are classified into top-of-canopy photons, canopy photons, and ground photons. The results show that the algorithm can effectively classify photons. Both estimated terrain height and canopy height derived using the classified photons are in good agreement with airborne LiDAR data. The mean absolute error (MAE) of the estimated terrain height relative to airborne data was 1.45 m and the root mean square error (RMSE) was 2.82 m. For canopy height validation, the correlation coefficient (R2), MAE, and RMSE at the best study scale (80 m) were 0.86, 1.82 m, and 2.72 m, respectively. These results demonstrated that the proposed LOFR algorithm can improve photon classification over complex terrain without prior knowledge of the terrain. Therefore, it could provide a robust approach for large-scale ATLAS data processing.

Linear uncertain modelling of LIDAR systems for robust wind turbine control design

Light detection and ranging (LIDAR) sensors measure the free wind ahead of the rotor, enabling the use of new feedforward control strategies. However, there exist some sources of error inherent to the measuring process that should be considered during the design of LIDAR-based controllers. Typically, the coherence function is used for that purpose, but it is not compatible with some robust design methodologies. This paper presents an analytic relation between the coherence function and a non-parametric uncertainty model of LIDAR sensors, suitable for the design of controllers via μ-synthesis or Quantitative Feedback Theory. Such a relation is applied to a realistic LIDAR simulator. First, the linear non-parametric uncertainty model is identified using simulation data obtained from the well-known NREL 5 MW wind turbine. Then, it is validated against the coherence model by comparing linear predictions of the simulation outputs.

Non-mechanical three-dimensional LiDAR system based on flash and beam-scanning dually modulated photonic crystal lasers

We propose and develop a new type of non-mechanical three-dimensional (3D) light detection and ranging (LiDAR) system, which integrates a beam-scanning-type laser source and a flash-type laser source. Employing on-chip dually modulated photonic crystal lasers for both flash and beam-scanning sources, we develop a LiDAR system that is small enough to fit in the palm of the hand. This system is capable of measuring poorly reflective objects in the field of view (FoV) by selectively illuminating these objects with sufficient power by the beam-scanning laser, thereby overcoming the intrinsic issue affecting conventional flash-type 3D LiDAR systems. In addition, we develop and implement a program to have the LiDAR system automatically recognize poorly reflective objects in the FoV and measure their distances by selective illumination. Furthermore, we use the LiDAR system to perform long-distance ranging while including a poorly reflective object in the FoV.

Monitoring of Water and Tillage Soil Erosion in Agricultural Basins, a Comparison of Measurements Acquired by Differential Interferometric Analysis of Sentinel TopSAR Images and a Terrestrial LIDAR System

Agricultural production, the main pillar of food security, is highly dependent on soil quality, and threatened by erosion processes that degrade soil quality. This article is part of a research to verify the usefulness of differential interferometric analysis on TopSAR (Terrain Observation with Progressive Scans SAR, Synthetic Aperture Radar) images to measure water and tillage erosion in small agricultural basins. For this, images from the Sentinel 1 mission are used, analyzing the deformations on the earth’s surface. The purpose of this research is to verify the accuracy of the proposed method by comparing its measures with the ones taken with the gold standard laser terrestrial LIDAR (Light Detection and Ranging) system, as well as to establish a basic step period framework that guarantees an admissible loss of coherence. The results on a pilot plot in El Molar (north of Madrid, Spain) showed that the differences lay within the range of the error associated with the very LIDAR system and showed that coherence losses correspond with the deformations measured. Given the economic and labor advantages of the differential interferometric analysis, this method could be regarded as an excellent alternative to the use of LIDAR in large-scale studies for measuring ground deformation caused by water and tillage erosion.

OCDMA Codeword Switching Technique to Avoid Interference of Time-of-Flight LiDAR System for Autonomous Vehicles

The direct time-of-flight (d-ToF) technique is the most promising method for light detection and ranging (LiDAR) systems in long-range autonomous applications. However, the receiver cannot distinguish its own pulse signal from those of other LiDAR systems. This causes mutual interference between LiDAR systems, which degrades object recognition. The optical code division multiple access (OCDMA) modulation technique can be used to avoid mutual interference. However, it is difficult to secure sufficient orthogonal codes, because each laser channel uses a designated codeword. In addition, when interference occurs in a specific direction, it can occur continuously. Adversarial attacks on a LiDAR system can trick a vehicle to perform false object recognition without physical contact. This can endanger passengers, pedestrians, and other vehicles. Therefore, we propose an OCDMA codeword switching technique that can effectively solve these problems in driving environments. First, we analyzed the characteristics of optical orthogonal codes (OOC) and defined the interference generated in LiDAR systems. The proposed technique exhibits interference robustness by using a code index buffer based on differences in the received optical signal power. In addition, verification of the hardware implementation and its complexity was analyzed using LiDAR testbench and an FPGA. The interference and continuous interference of the proposed technique in various driving environments were verified using simulation model. Compared with using only a designated codeword, the proposed technique reduces interference by 82.59% and 85.51%, and continuous interference by 92.24% and 96.94% in straight-line and curved driving, respectively.

Real-Time Chlorophyll-a Pigment Monitoring of Chlamydomonas reinhardtii in a Controlled Environment Using Pulsed LED Fluorescence LiDAR System

The real-time chlorophyll-a pigment monitoring of C. reinhardtii is studied using our developed LED fluorescence light detection and ranging (LiDAR) system. It features a portable set-up that uses a pulsed LED module with an excitation wavelength of 385 nm. We were able to monitor the different growth phases of C. reinhardtii with specific cultivation parameters. The developed fluorescence LiDAR system showed the linear correlation of its chlorophyll-a signal with the optical density and EEM fluorescence measurements at 680 nm emission wavelength. Water quality and weather parameters were also measured, which explains the variation in the growth dynamics of C. reinhardtii during the sampling period. The results from the monitoring demonstrated a different technique that can be used in estimating algal biomass in the environment.

Development of the high angular resolution 360° LiDAR based on scanning MEMS mirror

Light detection and ranging (LiDAR) using various operational principles has been applied in many fields, e.g., robotics navigation, autonomous vehicles, unmanned aerial flyers, land surveying, etc. The multichannel LiDAR system is of great importance in the field of autonomous driving due to its larger field of view (FoV). However, the number of transceivers limits the vertical angular resolution of multichannel LiDAR systems and makes them costly. On the other hand, the emergence of microelectromechanical systems (MEMS) mirrors may provide a highly promising solution to a low-cost, high angular resolution LiDAR system. We have demonstrated a MEMS mirror-based 360° LiDAR system with high angular resolution and will present the detailed design process and obtained experimental results in this paper. With the combination of the MEMS mirror and a rotation platform for the LiDAR system, a 360° × 8.6° (horizontal × vertical) FoV was achieved. Compared with existing commercial multichannel 360° LiDAR systems, our system has 13.8 times better angular resolution than the Velodyne HDL-64 LiDAR sensor. The experimental results verified an excellent performance of 0.07° × 0.027° (horizontal × vertical) angular resolution, which enhances the panoramic scanning and imaging capability of the LiDAR system, potentially providing more accurate 3D scanning applications in areas such as autonomous vehicles, indoor surveying, indoor robotics navigation, etc.