Light Detection And Ranging Data Processing Research Articles

LiDAR Data Processing for Digitization of the Castro of Santa Trega and Integration in Unreal Engine 5

ABSTRACT With the advancement of graphic engines, real-life structures can be digitized with more realistic representations than before. Virtual models obtained from LiDAR (Light Detection and Ranging) data in real-time applications can be inspected in graphic engines without rendering a point cloud. Well-known proprietary software is used to convert scanning from LiDAR into meshes of triangles that work the best on graphic pipelines. However proprietary software is usually expensive, hard to learn, and requires manual interaction. The proposed methodology generates virtual models from LiDAR with little manual interaction employing open-source software in an automated workflow for generic conversion. The point cloud is registered for geo-reference, processed for building textured models, and implemented in Unreal Engine 5 for Virtual Reality deployment. Specific improvements were made to the selected study case of the Castro of Santa Trega. Visualization of the model is overall more realistic than the rendering of every point in a cloud. The average framerate is improved upon a 229% when rendering optimized meshes compared to point clouds, leading to an enriched visualization quality and reduced data size. A Virtual Reality (VR) experience was implemented with an average of 143 FPS, surpassing the standard 90 FPS recommended to avoid motion sickness.

An experimental study on monitoring wave profiles with LiDAR

Light Detection and Ranging (LiDAR) has become increasingly popular for on-site wave monitoring due to its high temporal and spatial resolution. However, its potential application in the laboratory has not been well studied. In this study, a LiDAR and seven wave probes were used in a wave flume with a slope, to study the ability of LiDAR monitoring wave profiles in the laboratory. The results show good agreement between the time series of free surface measured by the LiDAR and wave probes, with an average error of less than 4%. Effective sampling ratio (the ratio of the number of LiDAR signals received per second to the sampling frequency at a point), relative wave height, positions of the breaker point and incident wave height are the key factors affecting LiDAR measurement error. When the value of effective sampling ratio is less than 0.8, it has dominant influence on the error. In shallow water, relative wave height is the dominant factor. A negative linear correlation is found between the measurement error and the incident wave height. These findings provide important guidance for processing and assessment of LiDAR data.

ICSF: An Improved Cloth Simulation Filtering Algorithm for Airborne LiDAR Data Based on Morphological Operations

Ground filtering is an essential step in airborne light detection and ranging (LiDAR) data processing in various applications. The cloth simulation filtering (CSF) algorithm has gained popularity because of its ease of use advantage. However, CSF has limitations in topographically and environmentally complex areas. Therefore, an improved CSF (ICSF) algorithm was developed in this study. ICSF uses morphological closing operations to initialize the cloth, and estimates the cloth rigidness for providing a more accurate reference terrain in various terrain characteristics. Moreover, terrain-adaptive height difference thresholds are developed for better filtering of airborne LiDAR point clouds. The performance of ICSF was assessed using International Society for Photogrammetry and Remote Sensing urban and rural samples and Open Topography forested samples. Results showed that ICSF can improve the filtering accuracy of CSF in the samples with various terrain and non-ground object characteristics, while maintaining the ease of use advantage of CSF. In urban and rural samples, ICSF obtained an average total error of 4.03% and outperformed another eight reference algorithms in terms of accuracy and robustness. In forested samples, ICSF produced more accuracy than the well-known filtering algorithms (including the maximum slope, progressive morphology, and cloth simulation filtering algorithms), and performed better with respect to the preservation of steep slopes and discontinuities and vegetation removal. Thus, the proposed algorithm can be used as an efficient tool for LiDAR data processing.

Adaptive estimation of UAV altitude in complex indoor environments using degraded and time-delayed measurements with time-varying uncertainties

A novel approach for robust Unmanned Aerial Vehicle (UAV) altitude estimation relying on laser measurements that is designed for use in complex indoor environments is proposed in this paper. Specifically, we aim to design a system with general usability inside multi-floor buildings. The multi-floor buildings are characterized by areas lacking distinct vertical geometric features to be used as reference by 3D Light Detection and Ranging (LiDAR) localization algorithms, and by areas with either flat floors or limited areas with inconsistent ground elevation. The proposed approach solves the problem of adaptive fusion of data from multiple sources with apriori-unknown confidence dependent on the current environmental properties. Whenever the environment contains enough geometric structure, altitude data from a 3D LiDAR-based Simultaneous Localization and Mapping (SLAM) algorithm are utilized. In environments that are too symmetrical for reliable SLAM operation, the approach relies mostly on measurements from a downward-facing 1D laser rangefinder, while simultaneously detecting inconsistent ground elevation areas. These measurements are fused with barometer data, Inertial Measurement Unit (IMU) data, and information from the UAV position controllers. Furthermore, our approach correctly handles the measurement delay caused by 3D LiDAR data processing that significantly differs from other sensor delays. The performance of the proposed approach has been validated in complex simulations and real-world experiments with the produced altitude estimate utilized in the control loop of the UAV. The proposed approach is released as open-source as part of the MRS UAV System.

Review of Automatic Processing of Topography and Surface Feature Identification LiDAR Data Using Machine Learning Techniques

Machine Learning (ML) applications on Light Detection And Ranging (LiDAR) data have provided promising results and thus this topic has been widely addressed in the literature during the last few years. This paper reviews the essential and the more recent completed studies in the topography and surface feature identification domain. Four areas, with respect to the suggested approaches, have been analyzed and discussed: the input data, the concepts of point cloud structure for applying ML, the ML techniques used, and the applications of ML on LiDAR data. Then, an overview is provided to underline the advantages and the disadvantages of this research axis. Despite the training data labelling problem, the calculation cost, and the undesirable shortcutting due to data downsampling, most of the proposed methods use supervised ML concepts to classify the downsampled LiDAR data. Furthermore, despite the occasional highly accurate results, in most cases the results still require filtering. In fact, a considerable number of adopted approaches use the same data structure concepts employed in image processing to profit from available informatics tools. Knowing that the LiDAR point clouds represent rich 3D data, more effort is needed to develop specialized processing tools.

Fast-Spherical-Projection-Based Point Cloud Clustering Algorithm

Roadside LiDAR (light detection and ranging) is a solution to fill in the gaps for connected vehicles (CV) by detecting the status of global road users at transportation facilities. It relies greatly on the clustering algorithm for accurate and rapid data processing so as to ensure effectiveness and reliability. To contribute to better roadside LiDAR-based transportation facilities, this paper presents a fast-spherical-projection-based clustering algorithm (FSPC) for real-time LiDAR data processing with higher clustering accuracy and noise handling. The FSPC is designed to work on a spherical map which could be directly derived from the instant returns of a LiDAR sensor. A 2D-window searching strategy is specifically designed to accelerate the computation and alleviate the density variation impact in the LiDAR point cloud. The test results show the proposed algorithm can achieve a high processing efficiency with 24.4 ms per frame, satisfying the real-time requirement for most common LiDAR applications (100 ms per frame), and it also ensures a high accuracy in object clustering, with 96%. Additionally, it is observed that the proposed FSPC allows a wider detection range and is more stable, tackling the surge in foreground points that frequently occurs in roadside LiDAR applications. Finally, the generality of the proposed FSPC indicates the proposed algorithm could also be implemented in other areas such as autonomous driving and remote sensing.

A dense background representation method for traffic surveillance based on roadside LiDAR

The performance of traditional video-based traffic surveillance systems is susceptible to illumination variation and perspective distortion. This has been a significant motivation in recent years for research into Light Detection and Ranging (LiDAR)-based traffic surveillance systems, as LiDAR is insensitive to both factors. The first step in LiDAR data processing involves effective extraction of moving foreground objects from a referenced background. However, existing methods only detect a static background based on LiDAR point density or relative distance. In this research, we develop a novel dense background representation model (DBRM) for stationary roadside LiDAR sensors to detect both static and dynamic backgrounds, for freeway traffic surveillance purposes. Background objects tend to be stationary in space and time. DBRM utilizes this property to detect two types of background: both static and dynamic. While the static background is represented by fixed structures, the dynamic background – which may be characterized by quasi-static objects such as tree foliage – is modeled by mixtures of Gaussian probability distributions. Experiments were carried out in two different scenarios to compare the proposed model with two other state-of-the-art models. The results demonstrate the effectiveness, robustness, and detail-preserving advantages of the proposed model.

Image-Aided LiDAR Mapping Platform and Data Processing Strategy for Stockpile Volume Estimation

Stockpile quantity monitoring is vital for agencies and businesses to maintain inventory of bulk material such as salt, sand, aggregate, lime, and many other materials commonly used in agriculture, highways, and industrial applications. Traditional approaches for volumetric assessment of bulk material stockpiles, e.g., truckload counting, are inaccurate and prone to cumulative errors over long time. Modern aerial and terrestrial remote sensing platforms equipped with camera and/or light detection and ranging (LiDAR) units have been increasingly popular for conducting high-fidelity geometric measurements. Current use of these sensing technologies for stockpile volume estimation is impacted by environmental conditions such as lack of global navigation satellite system (GNSS) signals, poor lighting, and/or featureless surfaces. This study addresses these limitations through a new mapping platform denoted as Stockpile Monitoring and Reporting Technology (SMART), which is designed and integrated as a time-efficient, cost-effective stockpile monitoring solution. The novel mapping framework is realized through camera and LiDAR data-fusion that facilitates stockpile volume estimation in challenging environmental conditions. LiDAR point clouds are derived through a sequence of data collections from different scans. In order to handle the sparse nature of the collected data at a given scan, an automated image-aided LiDAR coarse registration technique is developed followed by a new segmentation approach to derive features, which are used for fine registration. The resulting 3D point cloud is subsequently used for accurate volume estimation. Field surveys were conducted on stockpiles of varying size and shape complexity. Independent assessment of stockpile volume using terrestrial laser scanners (TLS) shows that the developed framework had close to 1% relative error.

LiDAR-INS/GNSS-Based Real-Time Ground Removal, Segmentation, and Georeferencing Framework for Smart Transportation

Light detection and ranging (LiDAR) sensor is attracting significant attention in the field of smart transportation because of its ability to give depth information. It is already extensively used for obstacle detection. The inertial navigation system (INS) with a global navigation satellite system (GNSS) gives the global position, orientation, and velocity of a given object in real time. In autonomous driving, LiDAR point clouds are segmented to find out the positions of various objects in the surrounding of the vehicle of interest. However, the obstacle position is determined in local east–north–up (ENU) coordinates. In this article, an end-to-end framework has been presented, which takes input from LiDAR and INS/GNSS systems and gives the obstacle position in universal coordinates (latitude, longitude) in real time. The proposed framework includes ground point removal from raw LiDAR data, obstacle segmentation, and LiDAR data fusion with INS/GNSS data for georeferencing. For LiDAR data processing, a novel method for removing ground points has been presented, in which the entire point cloud is divided into square grids in the horizontal plane. Based on the statistics of the vertical distribution of the points in each grid, ground points are identified. Two ground point datasets were also created for testing the proposed algorithm. These datasets contain point clouds of ground with varying inclination from various multichannel LiDARs. The proposed ground removal method was tested on the Paris-Lille-3D dataset and the dataset created. An F1-score of greater than 0.99 and 0.98 was achieved on our dataset and the Paris Lille-3D dataset, respectively. The framework is tested on different hardware configurations and found to be suitable for real-time applications.

AERIAL LiDAR TECHNOLOGY IN SUPPORT TO AVALANCHES PREVENTION AND RISK MITIGATION: AN OPERATIVE APPLICATION AT “COLLE DELLA MADDALENA” (ITALY)

Abstract. Snow avalanches are the result of unstable snow masses that detach from steep slopes as consequence of changes in snowpack structure. Nowadays, remote sensing technologies can improve the knowledge of avalanches phenomenon. This work focuses on the use of high point density aerial LiDAR (Light Detection And Ranging) technology as support to avalanche events prevention and risk mitigation, by presenting an operative application at Colle della Maddalena (Italy), along the road SS n. 21, nearby the French state border. The area is often involved in intense avalanche events that adversely impact on traffic and freight transport. For this reason, regional administrations will activate the Avalanche Artificial Detachment Intervention Plan (PIDAV, 2012) in order to prevent and manage the avalanche risk in the study area, also adopting artificial detachment systems. Main aim of the present work was to generate high resolution information related to geomorphological characterization (i.e. digital elevation models, slope and aspect) of avalanche sites derived from LiDAR data processing, that will help involved authorities in the management of the avalanche control plan. Digital elevation models at 0.5 m of spatial resolution were generated together with relative tridimensional models. Secondly, a preliminary investigation about capabilities and limits of LiDAR technology was done in the identification of avalanche sites only relying on geomorphological information directly derived by LiDAR data processing. Results showed that position of avalanche sites were correctly identified while no information could be obtained about the extension of the sliding area and identification of detachment areas.

Data Processing Approaches on SPAD-Based d-TOF LiDAR Systems: A Review

With the rise of advanced driver assistance systems (ADAS), range sensors and their data processing methods are becoming more and more important. Light detection and ranging (LiDAR) sensors are attracting attention due to their unique advantages in terms of radial distance resolution and detection range. However, the study of LiDAR data processing is usually divorced from the LiDAR sensor measurement process itself. This leads to critical measurement information being overlooked. This paper seeks a breakthrough to improve the performance of single-photon-avalanche-diode-based direct time-of-flight LiDAR systems by reviewing the data processing stages and corresponding processing approaches for LiDAR measurements, starting from photon incidence and ending with high-level feature recognition. Firstly, we propose a LiDAR system model based on data generation and transfer. The data forms in such a LiDAR system are mainly classified into timestamps, time-correlated histograms, point cloud data, and high-level properties. Subsequently, data processing methods applied to each of these data forms are analyzed. A number of hardware solutions closely related to data transmission and control are also included in the discussion. The principles, limitations, and challenges of these methods are discussed in detail and the criteria for evaluation of time-correlated histograms in ADAS are proposed. Finally, the research gaps in data processing are summarized, and future directions for research development are presented.

Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems

Autonomous vehicles rely on their perception systems to acquire information about their immediate surroundings. It is necessary to detect the presence of other vehicles, pedestrians and other relevant entities. Safety concerns and the need for accurate estimations have led to the introduction of Light Detection and Ranging (LiDAR) systems in complement to the camera or radar-based perception systems. This article presents a review of state-of-the-art automotive LiDAR technologies and the perception algorithms used with those technologies. LiDAR systems are introduced first by analyzing the main components, from laser transmitter to its beam scanning mechanism. Advantages/disadvantages and the current status of various solutions are introduced and compared. Then, the specific perception pipeline for LiDAR data processing, from an autonomous vehicle perspective is detailed. The model-driven approaches and the emerging deep learning solutions are reviewed. Finally, we provide an overview of the limitations, challenges and trends for automotive LiDARs and perception systems.

Automatic Processing of Aerial LiDAR Data to Detect Vegetation Continuity in the Surroundings of Roads

The optimization of forest management in the surroundings of roads is a necessary task in term of wildfire prevention and the mitigation of their effects. One of the reasons why a forest fire spreads is the presence of contiguous flammable material, both horizontally and vertically and, thus, vegetation management becomes essential in preventive actions. This work presents a methodology to detect the continuity of vegetation based on aerial Light Detection and Ranging (LiDAR) point clouds, in combination with point cloud processing techniques. Horizontal continuity is determined by calculating Cover Canopy Fraction (CCF). The results obtained show 50% of shrubs presence and 33% of trees presence in the selected case of study, with an error of 5.71%. Regarding vertical continuity, a forest structure composed of a single stratum represents 81% of the zone. In addition, the vegetation located in areas around the roads were mapped, taking into consideration the distances established in the applicable law. Analyses show that risky areas range from a total of 0.12 ha in a 2 m buffer and 0.48 ha in a 10 m buffer, representing a 2.4% and 9.5% of the total study area, respectively.

Accelerated LiDAR data processing algorithm for self‐driving cars on the heterogeneous computing platform

In recent years, light detection and ranging (LiDAR) has been widely used in the field of self-driving cars, and the LiDAR data processing algorithm is the core algorithm used for environment perception in self-driving cars. At the same time, the real-time performance of the LiDAR data processing algorithm is highly demanding in self-driving cars. The LiDAR point cloud is characterised by its high density and uneven distribution, which poses a severe challenge in the implementation and optimisation of data processing algorithms. In view of the distribution characteristics of LiDAR data and the characteristics of the data processing algorithm, this study completes the implementation and optimisation of the LiDAR data processing algorithm on an NVIDIA Tegra X2 computing platform and greatly improves the real-time performance of LiDAR data processing algorithms. The experimental results show that compared with an Intel® Core™ i7 industrial personal computer, the optimised algorithm improves feature extraction by nearly 4.5 times, obstacle clustering by nearly 3.5 times, and the performance of the whole algorithm by 2.3 times.

A Review of Progress and Applications of Pulsed Doppler Wind LiDARs

Doppler wind LiDAR (Light Detection And Ranging) makes use of the principle of optical Doppler shift between the reference and backscattered radiations to measure radial velocities at distances up to several kilometers above the ground. Such instruments promise some advantages, including its large scan volume, movability and provision of 3-dimensional wind measurements, as well as its relatively higher temporal and spatial resolution comparing with other measurement devices. In recent decades, Doppler LiDARs developed by scientific institutes and commercial companies have been well adopted in several real-life applications. Doppler LiDARs are installed in about a dozen airports to study aircraft-induced vortices and detect wind shears. In the wind energy industry, the Doppler LiDAR technique provides a promising alternative to in-situ techniques in wind energy assessment, turbine wake analysis and turbine control. Doppler LiDARs have also been applied in meteorological studies, such as observing boundary layers and tracking tropical cyclones. These applications demonstrate the capability of Doppler LiDARs for measuring backscatter coefficients and wind profiles. In addition, Doppler LiDAR measurements show considerable potential for validating and improving numerical models. It is expected that future development of the Doppler LiDAR technique and data processing algorithms will provide accurate measurements with high spatial and temporal resolutions under different environmental conditions.

Canopy detection over roads using mobile lidar data

ABSTRACT A high percentage of forest fires take place around roads. These infrastructures provide an escape route for the population in case of fire. Optimization of forest management in the surroundings of roads is a necessary task in terms of wildfires prevention and mitigation of their effects. Therefore, it is essential to avoid the horizontal continuity of vegetation across roads. A methodology for the measurement of canopy area over roads is developed and based on mobile LiDAR (Light Detection and Ranging) point clouds. The acquisition of LiDAR data is done by Lynx Mobile Mapper System from the University of Vigo. The methodology is automated using LiDAR data processing (M-estimator Sample Consensus and near neighbour algorithms) and image processing techniques (rasterization and binarization). The developed algorithms are tested on a study area, the DP-3606 road (Spain). Results are compared with ground truth data of the canopy projected on the road. The best obtained results present a mean geometric error of 2.82% for 0.25 m resolution and 104.02% for 2 m resolution. Furthermore, the higher the pixel size, the greater the error was obtained with a linear correlation value of 0.99.

Full-Waveform Airborne LiDAR Data Classification Using Convolutional Neural Networks

Point cloud classification is one of the most important and time-consuming stages of airborne LiDAR (Light Detection and Ranging) data processing, playing a key role in the generation of cartographic products. This paper describes an innovative algorithm to perform LiDAR point-cloud classification, which relies on Convolutional Neural Networks (CNNs) and takes advantage of full-waveform data registered by modern laser scanners. The proposed method consists of two steps. First, a simple CNN is used to preprocess each waveform, providing a compact representation of the data. By exploiting the coordinates of the points associated with the waveforms, output vectors generated by the first CNN are then mapped into an image that is subsequently segmented by a Fully Convolutional Network (FCN): a label is assigned to each pixel and, consequently, to the point falling in the pixel. In this way, spatial positions and geometrical relationships between neighboring data are taken into account. These particular architectures allow to accurately identify even challenging classes such as power line and transmission tower.

Automatic Vehicle Tracking With Roadside LiDAR Data for the Connected-Vehicles System

The existing connected-vehicle deployments obtain the real-time status of connected vehicles, but without knowing the unconnected traffic. It is urgent to find an approach to collecting the high-resolution real-time status of unconnected road users. This paper introduces a new-generation light detection and ranging (LiDAR) enhanced connected infrastructures that actively sense the high-resolution status of surrounding traffic participants with roadside LiDAR sensors and broadcast connected-vehicle messages through DSRC roadside units. The LiDAR data processing procedure, including background filtering, object clustering, vehicle recognition, lane identification, and vehicle tracking, is presented in this paper. The performance of the proposed data processing procedure is evaluated with the field collected data.

Automatic and Visual Processing Method of Non-Contact Monitoring for Circular Stormwater Sewage Tunnels Based on LiDAR Data

The application of Light Detection And Ranging (LiDAR) technology has become increasingly extensive in tunnel structure monitoring. The proposed processing method aims to carry out non-contact monitoring for circular stormwater sewage tunnels and provides an efficient workflow. This allows the automatic processing of raw point data and the acquisition of visualization results to analyze the health state of a tunnel within a short period of time. The proposed processing method employs a series of algorithms to extract the point cloud of a single tunnel segment without obvious noise by main three steps: axis acquisition, segment extraction, and denoising. The tunnel axis is extracted by fitting boundaries of the tunnel point cloud projection in the plane. With the guidance of the axis, the entire preprocessed tunnel point cloud is segmented by equal division to get a section of the tunnel point cloud which corresponds to a single tunnel segment. Then, the noise in every single point cloud segment is removed by clustering the algorithm twice, based on the distance and intensity. Finally, clean point clouds of tunnel segments are processed by an effective deformation extraction processor to determine the ovality and to get a three-dimensional visual deformation nephogram. The proposed method can significantly improve the efficiency of LiDAR data processing and extend the application of LiDAR technology in circular stormwater sewage tunnel monitoring.

Integration, Processing and Dissemination of LiDAR Data in a 3D Web-GIS

The rapid increase in applications of Light Detection and Ranging (LiDAR) scanners, followed by the development of various methods that are dedicated for survey data processing, visualization, and dissemination constituted the need of new open standards for storage and online distribution of collected three-dimensional data. However, over a decade of research in the area has resulted in a number of incompatible solutions that offer their own ways of disseminating results of LiDAR surveys (be it point clouds or reconstructed three-dimensional (3D) models) over the web. The article presents a unified system for remote processing, storage, visualization, and dissemination of 3D LiDAR survey data, including 3D model reconstruction. It is built with the use of open source technologies and employs open standards, such as 3D Tiles, LASer (LAS), and Object (OBJ) for data distribution. The system has been deployed for automatic organization, processing, and dissemination of LiDAR surveys that were performed in the city of Gdansk. The performance of the system has been measured using a selection of LiDAR datasets of various sizes. The system has shown to considerably simplify the process of data organization and integration, while also delivering tools for easy discovery, inspection, and acquisition of desired datasets.