Lidar Data Processing Research Articles

5G-Cloud-based real-time robotic part repairing for advanced manufacturing via computer vision

In this paper, we delve into the transformative capabilities of 5G wireless communication technology within the realm of robotics, particularly in the context of advanced manufacturing. Leveraging the power of computer vision and cloud/edge computing, we unveil the boundless possibilities that this technology heralds. Leveraging the ultra-reliable low latency communication (uRLLC) of 5G, we establish a distributed system showcasing its potential to revolutionize industrial processes. Additionally, to validate our framework, we developed a testbed tailored for real-world applications, focusing on milling operations using an industrial robot. Through high-level lidar data processing, we identify defects and create toolpaths, seamlessly to execute milling tasks. This research highlights 5G’s potential to redefine industrial processes while elucidating operational requirements for achieving uRLLC. Empirical testing underscores the benefits of 5G-robotics integration, enhancing efficiency and innovation in advanced manufacturing.

Simulations of the selection of solar plant characteristics in Baghdad using LiDAR data processing

Simulations of the selection of solar plant characteristics in Baghdad using LiDAR data processing

3D ToF LiDAR for Mobile Robotics in Harsh Environments: A Review

Over the past decade, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has grown rapidly. Based on our accumulation of relevant research, this paper systematically reviews and analyzes the use of 3D ToF LiDARs for mobile robotics under harsh conditions such as adverse weather, GPS-denied, and highly dynamic environments for both research and industrial applications. The former include LiDAR data processing in adverse weather, object detection, and autonomous navigation. The latter encompasses autonomous driving, service robotics, and public health crises applications. We hope that our efforts can effectively provide readers with a reference based on our hands-on experiences and promote the deployment of existing mature technologies in real-world systems.

Filtering airborne LiDAR data based on multi-view window and multi-resolution hierarchical cloth simulation

ABSTRACT Ground filtering is a fundamental step in airborne LiDAR data processing toward a variety of applications. However, existing algorithms remain tremendously challenging in complex environments, e.g. steep hillsides, ridges, valleys, discontinuities, and numerous objects. We presented a new ground filtering algorithm that can handle various landscapes. First, the multi-view window is developed to increase the number of ground seeds on the various terrains. Second, multi-resolution hierarchical cloth simulation is used to rapidly construct the high-resolution reference terrain, and bidirectional internal force operation is proposed to improve the accuracy of reference terrain by smoothing the spikes in cloth. Finally, ground and non-ground points are classified based on the height differences between points and the reference terrain. The proposed algorithm was validated not only in the International Society for Photogrammetry and Remote Sensing (ISPRS) but also karst datasets, where particularly complex environments is contained. Results showed that the proposed algorithm outperformed the existing algorithms, with the lowest average total error of 3.85% and the highest average kappa coefficient of 87.75%. Moreover, the proposed algorithm can completely preserve complex terrain, e.g. extremely steep hillsides, and sharp ridges. This study had great potential to provide a useful tool for LiDAR data processing.

CPU and GPU oriented optimizations for LiDAR data processing

Digital Terrain Models (DTM) can be accurately obtained from clouds of LiDAR points but the corresponding cloud processing time can be prohibitive. This paper describes several optimization techniques that have been applied to the Overlap Window Method (OWM) that is a key component in DTM applications. OWM was originally implemented in R which translates into serious limitations in terms of the size of the LiDAR point cloud that can be processed. We have ported the code to C++, significantly optimized the data structure to minimize memory accesses, and developed parallel implementations for CPU and GPU commodity devices using oneAPI libraries and tools. This results in CPU and GPU versions that are up to 19x and 83x faster, respectively, than an OpenMP baseline that uses eight CPU cores. Most importantly, the proposed optimizations for CPU and GPU can be paramount to get the most out of other LiDAR-based algorithms in which the careful selection of the right data structure, parallelization strategies and memory access reduction techniques will certainly result in significant performance improvements.

Marine profiling lidars and their application for oceanological problems

The review focuses on research conducted using profiling (radiometric) lidars. The paper presents the current state of lidar surveying equipment, methods for processing lidar data, and describes the problems of scientific and practical interest in oceanology that can be solved using lidar sensing. The review does not cover issues related to laser bathymetry, spectral (Raman) and spaceborne lidars, as they are separate specific fields. The main focus is on recent research in profiling lidar field. Summary tables of the technical characteristics of several of the most interesting airborne and shipborne lidars are provided. Their design features are discussed. Results from using lidars to determine near-surface hydrooptical characteristics, including employing polarization lidars and recently developed high-resolution spectral lidars, are presented. Findings from observing thin scattering layers across various aquatic regions are shown. The paper explores theoretical studies on lidar images of internal waves and experimental observations of internal waves in waters with different hydrooptical stratification. Lidars' application in addressing fisheries-related issues is examined. An overview of current development trends and future research directions is provided.

The performance of the digital city projects in urban studies of the megalopolises (the case studies of Kharkiv and Dnipro cities)

Introduction of the research problem. Urbanization drives Digital City Projects (DCPs) to create smarter urban environments using advanced technologies. DCPs aim to make cities more connected and responsive, adapting to changing needs. The objective of this paper is to evaluate the performance of DCPs in megalopolises, focusing on Kharkiv and Dnipro in Ukraine. The previous works done. The various literature sources demonstrate the rise of Digital Cities stemming from Smart Cities. Kharkiv and Dnipro in Ukraine exemplify digitalization's role amid Russian aggression. Exposition of the main research material. The performance of the theoretical urbogeosystemic approach and its UOM in the provision of practical Digital City projects. This subsection delves into the practical application of the urbogeosystemic approach and its Urban Ontological Model (UOM) in DCPs. The UOM guides urban studies by defining components and relationships. Implementing DCPs begins with building simulation models using LiDAR data. Case Study First - Kharkiv: A feasible perspective of a full-format DCP implementation. This subsection discusses implementing a DCP in Kharkiv, emphasizing data integration from OpenStreetMap (OSM) and LiDAR. The authors propose that a DCP should serve as a comprehensive model of a real city, encompassing all its structural elements and key objects, going beyond the capabilities of a typical GIS project. Possible user’s scenarios include energy consumption analysis, population estimation, and visibility gradients assessment. The subsection highlights the comprehensive DCP approach with LiDAR data processing software (iQ City CCM) and urban geosituational analysis. Case Study Second - Kharkiv: a perspective of geomarketing within the “Digital Kharkiv” project as a routine GIS one. This subsection delves into the integration of geomarketing into the "Digital Kharkiv" project. Geomarketing plays a pivotal role in mapping socioeconomic elements tied to market interactions. "Digital Kharkiv," primarily sourced from OSM data, is lauded for its versatility in urban studies during peacetime and war. The text urges exploration of geomarketing within "Digital Kharkiv" in the context of post-Russian aggression rehabilitation, particularly in optimizing humanitarian object placements. Changes in geomarketing potential pre- and post-invasion in various city districts have been analyzed, highlighting areas with stagnation and those witnessing growth due to population resettlement. Case Study Third - Dnipro: implementation of a typical GIS-project for analyzing provision of the city population with public transportation infrastructural networks. This subsection discusses the implementation of the "Digital Dnipro" project as part of the DCP framework. The project focuses on analyzing the provision of public transportation networks in the city of Dnipro. It utilizes data from OSM to create a virtual model of the city, which includes attribute information for urban objects. This subsection also highlights the impact of war on urban planning and the need for sustainable updates to adapt to changing conditions. Conclusion. This section summarizes the key findings and takeaways from the research on DCPs in Ukrainian cities like Kharkiv and Dnipro. It highlights the importance of an urbogeosystemic approach in implementing DCPs effectively. The study emphasizes the flexibility and efficiency of the relevant GIS tools in urban research and transformation.

Determination of the potential of solar energy, solar plant design, and grid integration based on LiDAR data processing in northern border region

Determination of the potential of solar energy, solar plant design, and grid integration based on LiDAR data processing in northern border region

Calculation of Resonance Fluorescence Scattering Cross Sections of Metal Particles in the Middle and Upper Atmosphere and Comparison of Their Detectability

Resonance fluorescence scattering is the physical mechanism with which lidar detects atmospheric metal layers. The resonance fluorescence scattering cross section is an important parameter for lidar data processing. In this work, the resonance fluorescence backscattering cross sections of most detectable metal atoms and ions in the atmosphere were calculated. The calculated maximum backscattering cross section of Na at the D2 line is 7.38 × 10−17 m2/sr; K at the D1 line is 7.37 × 10−17 m2/sr; Fe at the 372 nm line is 7.53 × 10−18 m2/sr; Fe at the 374 nm line is 6.98 × 10−18 m2/sr; Fe at the 386 nm line is 3.75 × 10−18 m2/sr; Ni at the 337 nm line is 4.05 × 10−18 m2/sr; and Ni at the 341 nm line is 2.05 × 10−17 m2/sr; Ca is 3.06 × 10−16 m2/sr; Ca+ is 1.12 × 10−16 m2/sr. The influence of the laser linewidth on the effective scattering cross section was discussed. If the laser linewidth is lower than 2 GHz to detect Na, the laser center frequency locked at the D2a line is a better option than the D2 line in order to obtain greater signals. If an unlocked lidar is used to detect Na, the frequency at D2a should be used as the laser center frequency when the effective scattering cross section of Na was calculated, because the absorption cross section of Na atom has two local maxima. This work proposes a quantifiable comparative method for assessing the observation difficulty of different metal particles by comparing their relative uncertainties in lidar observation. It is assumed that under the same observation conditions, the detectability of different metal atoms and ions is compared. Using Na as a basis for comparison, the relative uncertainty of Ni at 337 nm is the highest, about a factor of 21 larger than that of Na, indicating that it is the most difficult to be detected. The purpose of this work is to present a quantifiable comparison method for the detection difficulty of the metal particles by lidar in the middle and upper atmosphere, which has great significance for the design of the lidar system.

Energy management of plug-in hybrid electric vehicles based on speed prediction fused driving intention and LIDAR

Driving intention and speed prediction are essential factors in the energy management of plug-in hybrid electric vehicles (PHEVs). This paper proposes an improved energy management strategy for the subject vehicle by speed prediction fused with driving intention and LIDAR data in a vehicle-following scenario. A driving intention recognition model is developed based on the gated recurrent unit (GRU), which takes the vehicle speed, throttle opening, and brake pedal force of the subject vehicle as input. Then integrating the LIDAR point cloud data and driving intention result of the subject vehicle to achieve more accurate speed prediction, where joint probabilistic data association and interacting multiple models methods are used to process LIDAR data. The more accurate speed prediction is then applied to design a prediction-informed adaptive equivalent consumption minimization strategy (PIA-ECMS) for real-time energy management optimization. Experimental results demonstrate the recognition accuracy of up to 88%, indicating that the driver’s driving intention can be identified effectively. The speed prediction has an error margin of no more than 5.9 km/h. Compared with existing adaptive ECMS without speed prediction, the proposed PIA-ECMS can enhance fuel economy by 1.3–2.7% while achieving better SOC charge sustainability.

Adaptive LiDAR Reconstruction by Convolutional Compressive Sensing Network and Multivariate Empirical Mode Decomposition

With the recent advancement of LiDAR data processing pipelines integrated with signal processing techniques, it is now possible to rapidly acquire, process, and infer multidimensional information of 3D surroundings. This work proposes a novel compressive sensing (CS) framework (named MEMD-CSNet), based on Multivariate Empirical Mode Decomposition (MEMD) for adaptive 3D reconstruction of airborne urban LiDAR point clouds. EMD is a data-driven transform for decomposing the input data into Intrinsic Mode Functions (IMFs), capturing the implicit features in terms of variation in spatial frequency. Our approach generates a new filtered 3D signal constituting an optimal subset of IMFs based on the understanding of their statistical significance. Further, this filtered signal hierarchically extracts the overall shape in the last IMF (Residual function). Our proposed MEMD-CSNet is a deep convolutional network that (1) computes a multivariate EMD on input raw 3D LiDAR point clouds for generating data-dependent feature-rich filtered signal and (2) additionally augments this signal with convolutional features, thus learning a data-centric representation function (Ψ) for transforming the input into a low-dimensional sparse basis. We present extensive qualitative and quantitative analysis using the proposed MEMD-CSNet for a set of chosen CS measurement ratios. MEMD-CSNet achieves a maximum reconstruction PSNR of 108.22 dB (approx. 30 dB/1.36x better than without using MEMD) for reconstruction fidelity on selected urban LiDAR scenes. We envisage that our proposed MEMD-CSNet has a high potential (as a data-centric framework) for generating data-driven sparse feature maps and reconstructing entire 3D LiDAR point clouds from a few sparse measurements.

Processing of Lidar and IMU Data for Target Detection and Odometry of a Mobile Robot

In this paper, the processing of the data of a 3D light detection and distance measurement (LiDAR) sensor mounted on a mobile robot is shown, introducing an innovative methodology to manage the data and extracting useful information. The LiDAR sensor is placed on a mobile robot with a modular design that permits the easy change of the number of wheels, which was designed to travel through several environments, saving energy by preliminarily changing the number and arrangement of the wheels in the environment. Furthermore, the robot can recognise landmark in a structured environment by using a classification technique on each frame acquired by the LiDAR. Furthermore, considering the experimental tests, a new simple algorithm based on the LiDAR data processing together with the inertial data (IMU sensor) through a Kalman filter is proposed to characterise the robot’s pose by surrounding environment with fixed landmarks. Finally, the limits of the proposed algorithm have been analysed, highlighting new improvements in the future perspective development for permitting autonomous navigation and environment perception with a simple, modular, and low-cost device.

Machine-Learning-Method-Based Inversion of Shallow Bathymetric Maps Using ICESat-2 ATL03 Data

The application of empirical methods for satellite-derived bathymetry is limited by the lack of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> bathymetric data in remote, inaccessible areas. This challenge has been addressed with the launch of ICESat-2 (Ice, Cloud, and land Elevation Satellite-2). This study provides an accurate bathymetric photon extraction process for ICESat-2 ATL03 data, and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of the bathymetric photons obtained using this process and airborne bathymetric LiDAR data is up to 99%. Next, based on two types of remote sensing data, ICESat-2 and Sentinel-2, machine learning (ML) models, including linear regression (LR), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were trained to obtain bathymetric maps. The experimental results show that the mean RMSE (root mean square error), mean MAE (mean absolute error), and mean MRE (mean relative error) values of the LR models are less than 3.02 m, 2.38 m, and 86.03%, respectively. The mean RMSE, MAE, and MRE values of the LightGBM and CatBoost models are less than 0.91 m, 0.66 m, and 23.17%, respectively. It is concluded that the proposed denoising process for ICESat-2 ATL03 data is effective, and the results of the bathymetric maps obtained using these data are satisfactory. Thus, the proposed approach is effective, and this strategy can be used to replace conventional bathymetric inversion methods to obtain high-accuracy bathymetric maps.

KNN Based Denoising Algorithm for Photon-Counting LiDAR: Numerical Simulation and Parameter Optimization Design

Photon-counting LiDAR can obtain long-distance, high-precision target3D geographic information, but extracting high-precision signal photons from background noise photons is the key premise of photon-counting LiDAR data processing and application. This study proposes an adaptive noise filtering algorithm that adjusts parameters according to the background photon count rate and removes noise photons based on the local mean Euclidean distance. A simulated photon library that provides different background photon count rates and detection probabilities was constructed. It was then used to fit the distribution relationship between the background photon count rate and the average KNN (K-Nearest Neighbor) distance (k = 2–6) and to obtain the optimal denoising threshold under different background photon count rates. Finally, the proposed method was evaluated by comparing it with the modified density-based spatial clustering (mDBSCAN) and local distance-based statistical methods. The experimental results show that various methods are similar when the background noise rate is high. However, at most non-extreme background photon count rate levels, the F of this algorithm was maintained between 0.97–0.99, which is an improvement over other classical algorithms. The new strategy eliminated the artificial introduction of errors. Due to its low error rates, the proposed method can be widely applied in photon-counting LiDAR signal extraction under various conditions.

Field programmable gate array-based coherent lidar employing the ordinal statistics method for fast Doppler frequency determination

We report the development of a portable coherent Doppler lidar (CDL) system incorporating onboard field programmable gate array for real-time data processing. An ordinal statistic-based mean Doppler frequency estimation algorithm with high accuracy and low computational cost is proposed. The basic characteristics of the proposed method were specified with the use of mathematical modeling through the algorithm, realized as MATLAB computer code that includes parts simulating lidar signals and data processing. A comparison of the proposed method with established and well-used determination methods of the maximum Doppler frequency is accomplished. The ordinal statistics method is easy to implement and facilitates obtaining computational results swiftly. To demonstrate the performance of this lidar system, an intercomparison field campaign between lidar and ultrasonic anemometer is carried out. The excellent degree of correlation experimentally confirmed the feasibility of developing affordable, low-power consumption, compact, and robust CDL systems for various industrial and scientific applications.

Wildfires damage assessment Via LiDAR

This paper examines the phenomenon of wildfires in California and investigates the buildings affected by the Woolsey Fire in Central Malibu in 2018. We focus empirically on machine learning to identify damaged objects from point-cloud data. This project includes a literature review with references to methods used for wildfire research and LiDAR data processing. In this study, researchers trained an existing deep learning model to determine if it offers an effective solution for extracting damaged objects. Data sources for this study include point-cloud data retrieved via the LidarExplorer tool and Kaggle’s 2013–2020 California wildfire data. Using two layers of building footprints in the Malibu “T-Zone” revealed 907 structures, of which 435 were damaged or destroyed based on map observations. This analysis of structure identification supports the literature that deep learning can successfully classify objects damaged by wildfires.

Implementation of a program for real-time processing and visualization of LIDAR scan data in the LabVIEW programming environment

The features of development and optimization of software for real-time LIDAR data processing are considered. The advantages of the LabVIEW graphical development environment for creating highly optimized applications using parallel execution threads and pipelined data processing are shown.

Airborne LiDAR data in landscape archaeology. An introduction for non-archaeologists

Abstract The use of airborne LiDAR data has become an essential component of landscape archaeology. This review article provides an understandable introduction to airborne LiDAR data processing specific to archaeology with a holistic view from a technical perspective. It is aimed primarily at researchers, students, and experts whose primary field of study is not archaeology. The article first outlines what the archaeological interest in airborne LiDAR data is and how the data processing workflow is archaeology-specific. The article emphasises that the processing workflow is riddled with archaeology-specific details and presents the key processing steps. These are, in order of their impact on the final result, enhanced visualisation, manual reclassification, filtering of ground points, and interpolation. If a single most important characteristic of airborne LiDAR data processing for archaeology is to be emphasised, it is that archaeologists need an archaeology-specific DEM for their work.

Terraced Landscapes as NBSs for Geo-Hydrological Hazard Mitigation: Towards a Methodology for Debris and Soil Volume Estimations through a LiDAR Survey

Terraced landscapes are widely applied in many mountainous regions around the world as a result of the necessity to practice subsistence agriculture. Hence, they can be regarded as one of the most diffused anthropogenic modifications of the Earth’s surface. Different techniques have been used for their implementation leading to the artificial immobilization of debris and soil along the slopes whose surface is interrupted by a sequence of sub-horizontal and sub-vertical areas often using stone walls. In some areas of the world, such interventions are thousands of years old and their resistance to the degradation caused by the morphogenetic system can be attributed to the permeability of the stone walls as well as to their regular maintenance. In some other areas, the lack of maintenance has been the main cause for degradation processes ending with their collapse. The effects of climate change manifested through higher intensities and higher frequencies of rainfall are likely to accelerate the degradation process further by causing terraces to act as a source of debris or hyperconcentrated flow. This will in turn increase the severity of geo-hydrological hazards. The measures concerning reduction of geo-hydrological hazards are sought through identification of abandoned terraces and assessment of the potential for their sudden collapse. The present paper describes a framework for identification of abandoned terraces and estimation of the potential volume of shallow landslides that can be generated. The research conducted aims to advance the existing hazard assessment practices by combining numerical modeling with processing of high-resolution LiDAR data. A new algorithm is developed to support localization of terraces. The catchment scale approach applied to eight smaller catchments enables estimation of the total volume of soil and debris trapped along the slopes. It also generated some important quantitative data which will be used in the future risk assessment work. The work has been carried out within the EU-funded H2020 project RECONECT.

A NEW FRAMEWORK FOR ACCURACY ASSESSMENT OF LIDAR-DERIVED DIGITAL ELEVATION MODELS

Abstract. Existing approaches to accuracy assessment of LiDAR-derived DEM are typically based on statistical methodology and focus on vertical accuracy. This paper presents a new framework which calls for assessment of not only a DEM’s vertical accuracy, but also its ability to preserve a point’s elevation rank in the bare earth topographic surface. New methods to assess each aspect are presented. For DEM’s vertical accuracy, approximation theory from numerical analysis is used to quantify the total error at each DEM point as well as its three error components – sensor error, ground error, and interpolation error. For DEM’s elevation order which is critical to model terrain structure, the concept of isomorphism in set theory is drawn as the mathematical rationale and Kendall’s rank correlation efficient is used to quantify the accuracy. The new framework is illustrated using a DEM derived from LiDAR for sea-level rise vulnerability assessment of a tidal salt marsh. Compared to conventional methods based on statistics, the new framework and methods produce detailed mapping of error distribution thus enable the identification of the main sources of error and where improvement is needed most. Results call for re-evaluation of the current practice of assessing filtering accuracy in LiDAR data processing and further research on relative elevation and isomorphism.