Light Detection And Ranging Technology Research Articles

Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion

Depth estimation represents a prevalent research focus within the realm of computer vision. Existing depth estimation methodologies utilizing LiDAR (Light Detection and Ranging) technology typically obtain sparse depth data and are associated with elevated hardware expenses. Multi-view image-matching techniques necessitate prior knowledge of camera intrinsic parameters and frequently encounter challenges such as depth inconsistency, loss of details, and the blurring of edges. To tackle these challenges, the present study introduces a monocular depth estimation approach based on an end-to-end convolutional neural network. Specifically, a DNET backbone has been developed, incorporating dilated convolution and feature fusion mechanisms within the network architecture. By integrating semantic information from various receptive fields and levels, the model’s capacity for feature extraction is augmented, thereby enhancing its sensitivity to nuanced depth variations within the image. Furthermore, we introduce a loss function optimization algorithm specifically designed to address class imbalance, thereby enhancing the overall predictive accuracy of the model. Training and validation conducted on the NYU Depth-v2 (New York University Depth Dataset Version 2) and KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) datasets demonstrate that our approach outperforms other algorithms in terms of various evaluation metrics.

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.

System for Detecting Moving Objects Using 3D Li-DAR Technology

The “System for Detecting Moving Objects Using 3D LiDAR Technology” introduces a groundbreaking method for precisely identifying and tracking dynamic entities within a given environment. By harnessing the capabilities of advanced 3D LiDAR (Light Detection and Ranging) technology, the system constructs intricate three-dimensional maps of the surroundings using laser beams. Through continuous analysis of the evolving data, the system adeptly discerns and monitors the movement of objects within its designated area. What sets this system apart is its innovative integration of a multi-sensor approach, combining LiDAR data with inputs from various other sensor modalities. This fusion of data not only enhances accuracy but also significantly boosts the system’s adaptability, ensuring robust performance even in challenging environmental conditions characterized by low visibility or erratic movement patterns. This pioneering approach fundamentally improves the precision and reliability of moving object detection, thereby offering a valuable solution for a diverse array of applications, including autonomous vehicles, surveillance, and robotics.

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.

Forestry Applications of Space-Borne LiDAR Sensors: A Worldwide Bibliometric Analysis.

The 21st century has seen the launch of new space-borne sensors based on LiDAR (light detection and ranging) technology developed in the second half of the 20th century. Nowadays, these sensors offer novel opportunities for mapping terrain and canopy heights and estimating aboveground biomass (AGB) across local to regional scales. This study aims to analyze the scientific impact of these sensors on large-scale forest mapping to retrieve 3D canopy information, monitor forest degradation, estimate AGB, and model key ecosystem variables such as primary productivity and biodiversity. A worldwide bibliometric analysis of this topic was carried out based on up to 412 publications indexed in the Scopus database during the period 2004-2022. The results showed that the number of published documents increased exponentially in the last five years, coinciding with the commissioning of two new LiDAR space missions: Ice, Cloud, and Land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). These missions have been providing data since 2018 and 2019, respectively. The journal that demonstrated the highest productivity in this field was "Remote Sensing" and among the leading contributors, the top five countries in terms of publications were the USA, China, the UK, France, and Germany. The upward trajectory in the number of publications categorizes this subject as a highly trending research topic, particularly in the context of improving forest resource management and participating in global climate treaty frameworks that require monitoring and reporting on forest carbon stocks. In this context, the integration of space-borne data, including imagery, SAR, and LiDAR, is anticipated to steer the trajectory of this research in the upcoming years.

Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data

Forests play a significant role in sequestering carbon and regulating the global carbon and energy cycles. Accurately estimating forest biomass is crucial for understanding carbon stock and sequestration, forest degradation, and climate change mitigation. This study was conducted to estimate above-ground biomass (AGB) and compare the accuracy of the AGB estimating models using LiDAR (light detection and ranging) data and forest inventory data in the central Terai region of Nepal. Airborne LiDAR data were collected in 2021 and made available by Nepal Ban Nigam Limited, Government of Nepal. Thirty-two metrics derived from the laser-scanned LiDAR point cloud data were used as predictor variables (independent variables), while the AGB calculated from field data at the plot level served as the response variable (dependent variable). The predictor variables in this study were LiDAR-based height and canopy metrics. Two statistical methods, the stepwise linear regression (LR) and the random forest (RF) models, were used to estimate forest AGB. The output was an accurate map of AGB for each model. The RF method demonstrated better precision compared to the stepwise LR model, as the R2 metric increased from 0.65 to 0.85, while the RMSE values decreased correspondingly from 105.88 to 60.9 ton/ha. The estimated AGB density varies from 0 to 446 ton/ha among the sample plots. This study revealed that the height-based LiDAR metrics, such as height percentile or maximum height, can accurately and precisely predict AGB quantities in tropical forests. Consequently, we confidently assert that substantial potential exists to monitor AGB levels in forests effectively by employing airborne LiDAR technology in combination with field inventory data.

Angle Assessment for Upper Limb Rehabilitation: A Novel Light Detection and Ranging (LiDAR)-Based Approach.

The accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost-benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.

Hostage Liberation Operations using Wheeled Robots Based on LIDAR (Light Detection and Ranging) Sensors

Hostage release operations require a high level of precision, alertness, and skill, which are carried out manually by soldiers of the Indonesian National Army. This medium presents a significant risk to soldiers. This research aims to improve the effectiveness of hostage release operations by integrating wheeled robot technology based on Light Detection and Ranging (LIDAR) sensors. The research method used is experiment-based in developing and testing a prototype of a mobile robot equipped with LIDAR technology and a web camera capable of mapping the location of hostages in three dimensions. The research showed that this robot has high accuracy, reaching 97.87%, and can createthree-dimensional route maps and display real-time video on a computer. The use of this technology has the potential to reduce risks to soldiers and improve the accuracy of mapping hostage locations, which can ultimately improve the safety and effectiveness of hostage release operations in the context of special operations tasks by soldiers of the Army.

Postfire damage zoning with open low-density LiDAR data sources in semi-arid forests of the Iberian Peninsula

Wildfires represent one of the major ecological disasters with significant repercussions for terrestrial ecosystems (loss of biodiversity, material damages, erosion, etc). Using LiDAR (Light Detection and Ranging) technology allows large amounts of forest measurements data to be obtained, such as size of volume, biomass, canopy cover, heights, among others. It also allows to quantify some damage caused by forest fires by comparing LiDAR data point clouds at two different times (before and after fire). By correlating in situ measurements with statistical parameters from point clouds, we aim to evaluate postfire deviations in volume, biomass and canopy integrity. The aim of this study was to estimate the losses caused by the 2012 wildfire in the Public Utility Forest No.82 “Sierra de los Donceles” (Hellín, Albacete, Spain), which devastated more than 5000 ha. We used 65 plots from the 4th National Forest Inventory (NFI4) and the LiDAR data from 2009 to develop statistical models and train Artificial Neural Networks (ANN). Another independent set of 29 plots (of 14.1-m radius with sub-metric GPS) was employed to validate the regression and ANN models. The regression results showed better performance for estimating heights (R2 = 75.03%, RMSE = 28.90%), volume (R2 = 70.32%, RMSE = 37.61%) and basal area (R2 = 68.37%, RMSE = 37.18). The total biomass showed the best fit (R2 = 93.51%), but also the maximum error (RMSE = 40.08%). The worst fit result was the estimation of number of trees per hectare (R2 = 33.76%, RMSE = 30.97%). All the models were improved with neural network implementation: number of trees per hectare (R2 = 44.41%, RMSE = 26.74%), basal area (R2 = 68.81%, RMSE = 33.49%), dominant height (R2 = 75.55%, RMSE = 15.43%), volume (R2 = 61.03%, RMSE = 14.78%) and biomass (R2 = 99.21%, RMSE = 20.81%). With our top-performing models in the 2009- and 2016-point cloud data, we measured forest reductions in terms of trees per hectare, basal area, predominant height, volume and biomass. These models provided a comprehensive view of forest losses by highlighting major decreases in vegetation coverage, tree density and tree heights.

Modelling Small Artefact for Preservation – A Case Study of Perlis Heritage

Heritage preservation is essential for preserving historical sites and cultural artefact for future generations. However, they are prone to damages and destructions due to weather conditions and other factors. 3D models and reconstructions can aid in the conservation of historical sites and artefact. LiDAR (light detection and ranging) technology can be utilized to obtain accurate 3D representation of object or area of interest. This project aims to preserve one of the historical artefact in Perlis, Malaysia which is known as ‘Batu Nisan Acheh’ or the Acheh Gravestone by using 3D model and reconstruction. iPhone 13 Pro Max LiDAR scanner is used to collect the raw dataset of the artefact with Scaniverse application. MATLAB is employed for data processing which includes data filtering, noise reduction, downsampling and 3D surface reconstruction. In addition, a GUI application is also developed in enabling users to upload their desired point cloud files and produce its 3D model for future usage. Results show that the accuracy, effectiveness, and usability of heritage preservation initiatives are improved by combining iPhone 13 Pro Max LiDAR scanning with MATLAB processing, which is useful for virtual displays, restoration, and future study.

Bathymetric DEM derived from Lyzenga’s algorithm (multispectral Bathymetry) using Landsat 8 images: a case study of Fort Lauderdale coast

Optical bathymetry can be applied to multispectral images of coastal regions with low spectral and spatial resolutions, and the results obtained are satisfactory in waters up to 15 m. However, the limitations and effectiveness may vary as they are related to physical factors such as water turbidity, specular reflection, and the presence of clouds and shadows during imagery acquisition. Some of these factors also affect the surveys conducted using green Light Detection and Ranging (LiDAR) technology. This study aimed to show that topographic representation by multispectral images is directly related to the physical factors of the water body and the atmospheric conditions during the image acquisition process, good atmospheric conditions and shallow water can generate accurate topographic representation. The experiments carried out in this study using optical bathymetry showed that Landsat 8 satellite images could be used in multispectral bathymetry with a linear regression model by using depth samples obtained from shallow water. Images taken at three different time points were used, with a certain time interval between the data collection performed via the Green LiDAR technology. The depths estimated by this model showed some differences that were possibly caused by water turbidity and temporally-occurring activities. Despite such constraints, the depths obtained by the linear regression model showed a good adaptation to the model derived from the Green LiDAR for depths up to 22 m, with an average error of -0.271 m and a root mean square error of 0.936 m.

LIDAR-Based Forest Biomass Remote Sensing: A Review of Metrics, Methods, and Assessment Criteria for the Selection of Allometric Equations

The increasing level of atmospheric carbon dioxide and its effects on our climate system has become a global environment issue. The forest ecosystem is essential for the stability of carbon in the atmosphere as it operates as a carbon sink and provides a habitat for numerous species. Therefore, our understanding of the structural elements of the forest ecosystem is vital for the estimation of forest biomass or terrestrial carbon stocks. Over the last two decades, light detection and ranging (LIDAR) technology has significantly revolutionized our understanding of forest structures and enhanced our ability to monitor forest biomass. This paper presents a review of metrics for forest biomass estimation, outlines metrics selection methods for biomass modeling, and addresses various assessment criteria for the selection of allometric equations for the aboveground forest biomass estimations, using LIDAR data. After examining one hundred publications written by different authors between 1999 and 2023, it was observed that LIDAR technology has become a dominant data collection tool for aboveground biomass estimation with most studies focusing on the use of airborne LIDAR data for the plot-level analysis on a local scale. Parametric-based models dominated in most studies with coefficient of determination (R2) and root mean square error (RMSE) as assessment criteria. In addition, mean top canopy height (MCH) and quadratic mean height (QMH) were reported as strong predictors for aboveground biomass (AGB) estimation. Pixel-based uncertainty analysis was found to be a reliable method for assessing spatial variations in uncertainties.

Monitoring Spatiotemporal Variation of Individual Tree Biomass Using Multitemporal LiDAR Data

Assessing the spatiotemporal changes in forest aboveground biomass (AGB) provides crucial insights for effective forest carbon stock management, an accurate estimation of forest carbon uptake and release balance, and a deeper understanding of forest dynamics and climate responses. However, existing research in this field often lacks a comprehensive methodology for capturing tree-level AGB dynamics using multitemporal remote sensing techniques. In this study, we quantitatively characterized spatiotemporal variations of tree-level AGB in boreal natural secondary forests in the Greater Khingan Mountains region using multitemporal light detection and ranging (LiDAR) data acquired in 2012, 2016, and 2022. Our methodology emphasized improving the accuracy of individual tree segmentation algorithms by taking advantage of canopy structure heterogeneity. We introduced a novel three-dimensional metric, similar to crown width, integrated with tree height to calculate tree-level AGB. Moreover, we address the challenge of underestimating tree-level metrics resulting from low pulse density, ensuring accurate monitoring of AGB changes for every two acquisitions. The results showed that the LiDAR-based ΔAGB explained 62% to 70% of the variance of field-measured ΔAGB at the tree level. Furthermore, when aggregating the tree-level AGB estimates to the plot level, the results also exhibited robust and reasonable accuracy. We identified the average annual change in tree-level AGB and tree height across the study region, quantifying them at 2.23 kg and 0.25 m, respectively. Furthermore, we highlighted the importance of the Gini coefficient, which represents canopy structure heterogeneity, as a key environmental factor that explains relative AGB change rates at the plot level. Our contribution lies in proposing a comprehensive framework for analyzing tree-level AGB dynamics using multitemporal LiDAR data, paving the way for a nuanced understanding of fine-scale forest dynamics. We argue that LiDAR technology is becoming increasingly valuable in monitoring tree dynamics, enabling the application of high-resolution ecosystem dynamics products to elucidate ecological issues and address environmental challenges.

Methods and Applications of 3D Ground Crop Analysis Using LiDAR Technology: A Survey.

Light Detection and Ranging (LiDAR) technology is positioning itself as one of the most effective non-destructive methods to collect accurate information on ground crop fields, as the analysis of the three-dimensional models that can be generated with it allows for quickly measuring several key parameters (such as yield estimations, aboveground biomass, vegetation indexes estimation, perform plant phenotyping, and automatic control of agriculture robots or machinery, among others). In this survey, we systematically analyze 53 research papers published between 2005 and 2022 that involve significant use of the LiDAR technology applied to the three-dimensional analysis of ground crops. Different dimensions are identified for classifying the surveyed papers (including application areas, crop species under study, LiDAR scanner technologies, mounting platform technologies, and the use of additional instrumentation and software tools). From our survey, we draw relevant conclusions about the use of LiDAR technologies, such as identifying a hierarchy of different scanning platforms and their frequency of use as well as establishing the trade-off between the economic costs of deploying LiDAR and the agronomically relevant information that effectively can be acquired. We also conclude that none of the approaches under analysis tackles the problem associated with working with multiple species with the same setup and configuration, which shows the need for instrument calibration and algorithmic fine tuning for an effective application of this technology.

A study on meta-analysis approach for pedestrian-vehicle interaction using LiDAR

To analyse the continuous and intricate interaction between pedestrians and vehicles and to safeguard them, high-resolution micro-traffic data is needed. In comparison to other traditional means of data collection, light detection and ranging (LiDAR) technology is gaining popularity since it can deliver this information with less computing effort and is weather-resistant. This article uses LiDAR technology to analyse qualitative and quantitative data related to pedestrian-vehicle interaction. Based on the study selection criteria, a total of 39 studies were included in the qualitative analysis. Based on the availability of the data, four of these studies were then included in the quantitative meta-analysis. Some of the algorithms developed thus far have a 99 percent accuracy rate. The analysis was carried out using the Fisher r-to-z transformed correlation coefficient as the outcome measure. The amount of heterogeneity was estimated using the restricted maximum-likelihood estimator. A random-effects model was fitted to the data. Q = 51.716, p = 0.0001, and I2 = 92.86 percent were the results of the Q test, demonstrating that there is enough heterogeneity amongst the studies. Neither the rank correlation nor the regression test indicated any funnel plot asymmetry. The findings indicate that LiDAR is a suitable technology for studying pedestrian-vehicle interaction. The study results are useful to explore the new technology of LiDAR and its suitability in pedestrian-vehicle interactions.

Wide field of view optical phased array with a high-directionality antenna.

Light detection and ranging (LiDAR) is a widely utilized technology for extracting information from the outside world in fields such as automotive, robotics, and aerospace. Optical phased array (OPA) is a promising solution for LiDAR technology, although its application is limited by loss and alias-free steering range. In this paper, we propose a dual-layer antenna that achieves a peak directionality of over 92%, thereby mitigating antenna loss and enhancing power efficiency. Based on this antenna, we design and fabricate a 256-channel non-uniform OPA that achieves 150° alias-free steering.

Development of Cloud Point Data Processing Program for 3D BIM and 2D Cross Section Needs

The need for technological developments is needed to facilitate performance, accuracy, and effectiveness of work, especially in the field of civil engineering, is needed. With the emergence of innovative LiDAR (Light Detection and Ranging) technology scanners that are popularly used for 3D printing, developed into LiDAR Scanners for real field scanning. The result of using a LiDAR Scanner is in the form of point cloud data in a certain format, with a large enough memory. The purpose of this research is to use field point cloud data as 3D BIM data and then form a cross-section of the object. For this purpose, a special program is needed that functions to process cloud point data complexly, and is easy to use to change the shape of cloud point data to 3D data surface and 2D cross sections. The method used in this study is by creating a special program to process data point clouds using script code with the python language and several data point cloud processing libraries. In the program, 2 sub-menus will be created with certain functions: 1) Point Cloud (voxel downsampling, outlier reduction, normalize); 2) 3D model (ball pivoting/poisson surface, reduce vertex, slice mesh, transform mesh). In each data processing, the created program can only process on a specific file format; for point cloud processing in .xyz, .xyzn, .xyzrgb, .pts, .ply, .pcd formats; while for 3D data processing models are in .ply, .stl, .obj, .off , .gltf/glb format. The result of data processing using the created program can be a 3D surface with .ply /.obj format, and for cross-section generated 2D data with .jpg / .png format, and can be in the form of .dxf data for Autocad software. 3D surface data can be used as BIM data, while 2D cross-section data can be used as built 2D.

ROAD SURFACE MODELLING AND CHARACTERIZATION FROM TERRESTRIAL LIDAR DATA

Abstract. The general purpose of the paper is the study of surveying and data processing methodologies that are efficient to obtain more detailed metric data on road infrastructures than can be derived from classical surveying techniques. The inspection and monitoring of the condition of an infrastructure are two essential steps to increase the users' safety and to properly manage the available resources and are a preparatory step to the subsequent steps of deciding on the interventions to be put in place. Analysis of the state of degradation, if conducted with traditional methodologies, can be risky and sometimes inefficient. The Mobile Laser Scanner (MLS) technique, based on LiDAR (Light Detection and Ranging) technology, is also widely used today as an alternative to traditional techniques since it allows obtaining dense and accurate point clouds of the road surface. The purpose of our work is to provide a workflow for the processing of MLS data aimed at producing some useful indicators to describe the functional and structural characteristics of the pavement, with the goal of optimizing the decision-making processes of the Managing Authority. Specifically, the data flow was studied, and several processing algorithms were implemented to identify and quantify surface defects and road roughness. The result of the entire process is the creation of an Atlas in QGIS to create graphical tables related to each individual cross profile and that can be used to identify all those sections that need emergency actions and therefore characterized by a high priority of intervention.

Geometric and strain behaviour of full-scale geotextile tubes for dewatering sludge

Geotextile tubes have been presented as a viable alternative for dewatering sludge generated in different industries, characterised by having fine grain and high moisture content. The material commonly used to develop this system is woven geotextile due to its high strength properties. However, nonwoven geotextile tubes are a possible underexplored alternative that have good cost benefits. This paper presents the geometric and strain behaviour of two full-scale nonwoven geotextile tubes used for dewatering water treatment plant (WTP) sludge. Four technologies were used to monitor the full-scale tests: ultrasonic sensor (US), perspective laser tracking (PLT), light detection and ranging (LIDAR) and draw wire sensor (DWS). The geometric parameters of a quarter of the cross-section and the strains in the geotextile were obtained during sequential fills. These data were compared with results obtained from an analytical method based on membrane theory that considers the non-linear-elastic behaviour of the geotextile. The DWS and LIDAR technologies were the most suitable for monitoring the strains and shapes of the tubes, respectively. Good agreement was found between analytical and experimental results, indicating the applicability of the adopted method to the design of the first filling cycle of the monitored nonwoven geotextile tubes.

Demonstration of high-accuracy 3D imaging using a Si optical phased array with a tunable radiator.

Precise imaging in three-dimension (3D) is an essential technique for solid-state light detection and ranging (LiDAR). Among various solid-state LiDAR technologies, silicon (Si) optical phased array (OPA)-based LiDAR has the significant advantage of robust 3D imaging due to its high scanning speed, low power consumption, and compactness. Numerous techniques employing a Si OPA have utilized two-dimensional arrays or wavelength tuning for longitudinal scanning but the operation of those systems is restricted by additional requirements. Here, we demonstrate high-accuracy 3D imaging using a Si OPA with a tunable radiator. As we adapted a time-of-flight approach for distance measurement, we have developed an optical pulse modulator that allows a ranging accuracy of less than 2 cm. The implemented Si OPA is composed of an input grating coupler, multimode interferometers, electro-optic p-i-n phase shifters, and thermo-optic n-i-n tunable radiators. With this system, it is possible to attain a wide beam steering range of 45° in a transversal angle with a 0.7° divergence angle, and 10° in a longitudinal angle with a 0.6° divergence angle can be achieved using Si OPA. The character toy model was successfully imaged in three dimensions with a range resolution of 2 cm using the Si OPA. The further improvement of each component of the Si OPA will allow even more accurate 3D imaging over a longer distance.