Lidar Data Processing Research Articles

Automatic Lane Identification Using the Roadside LiDAR Sensors

How to collect the real-time information of unconnected vehicles has been a challenge for connected vehicle technologies. The LiDAR sensors deployed along the roadside and at intersections provide a solution to fill the data gap during the transition from the traditional traffic to the full connected traffic. The roadside LiDAR sensors can record the movement of all road users with a relative long detection range. The lane detection serves as a fundamental but important step for LiDAR data processing. The location (which lane is occupied) of vehicles can be used for lane changing detection, vehicle departure warning and wrong-way alerts. But currently, there is not an effective method to identify the boundary of lanes using roadside LiDAR sensors. This paper presents a systematic procedure for lane detection based on the trajectories of vehicles collected on the road with the roadside LiDAR sensor. The whole procedure includes two major parts: background filtering and road boundary identification. This robust procedure can release engineers from the manual lane identification task and can avoid any error caused by manual work. Two case studies confirmed the effectiveness of the proposed method. Compared to previous lane detection methods, this procedure is not affected by the existence of pedestrians. This method can also detect the boundaries of lanes from the roads having curves with the limited time cost.

Analysis of deriving control points from LiDAR intensity image for orthophoto production

In aerial photogrammetry need control points and has a reference to the land coordinates where the measurement location is carried out. This control point would be used in aerial photo georeferenced. But in practice it often finds severe field conditions that can affect the process of measuring control points, such a forest, mountains area and even conflict areas, which could make the process of measurement at that location impossible. LiDAR Intensity Image is one product of processing LiDAR data that can be used to produce coordinates of ground control points. So that it can reduce or eliminate the use of coordinates from GCP point measurements in the field of georeferenced in aerial photographs. This research is calculating the accuracy orthophoto produced of control points from LiDAR Intensity Image. The data used are LiDAR data and aerial photography using the Bundle Block Adjustment method. The result is orthophoto which tested for accuracy based on PERKA BIG No. 15 of 2014. The results from the CE90 and LE90 tests for orthophoto that use control points from the Premark Intensity Image are 0.433 m and 0.479 m which means fulfilling the scale 1:2500 class 1 for horizontal and vertical accuracy. For the CE90 and LE90 tests that use control points from a combination of intensity image and GCP points from GPS measurements, produce better accuracy of 0.314 m and 0.301 m so that fulfilling the scale 1:2500 class 1 for horizontal and vertical accuracy.

Multi-scale roof characterization from LiDAR data and aerial orthoimagery: Automatic computation of building photovoltaic capacity

Abstract Photovoltaic self-consumption in buildings requires the installation of photovoltaic (PV) systems mostly on roofs, taking the advantage of these building locations for both the available surface area and the certainty of a big amount of annual incoming solar radiation. Since the parameters required for a proper PV system design are mainly geometric: azimuth-orientation, tilt angle and effective dimensions of the different roof slopes; aerial LiDAR data and orthoimagery offered by the National Geographic Agencies became suitable data sources for this purpose, ensuring its availability for any city regardless of its location. This paper presents a novel automatic methodology that combines LiDAR and orthoimage data processing to geometrically characterize roofs at slope level and calculate their PV solar potential. The methodology developed has been validated against results obtained from a higher-resolution aerial 3D point cloud of the roofs under study. Different locations and roof types have been tested in order to confirm the performance of the methodology under different conditions, being able to accurately characterize the geometry of most types of roofs, such as flat roofs, gable or saddle roofs, single pitched roofs and pyramid roofs at city, neighbourhood and building level.

Robust Lidar Data Processing and Quality Control Methods Developed for the SWiFT Wake Steering Experiment

Sandia National Laboratories and the National Renewable Energy Laboratory conducted a wake-steering field experiment at the Scaled Wind Farm Technology facility. The campaign included the use of two highly instrumented V27 wind turbines, an upstream meteorological tower, and high-resolution wake measurements of the upstream wind turbine using a customized scanning SpinnerLidar from the Technical University of Denmark. The present work details how the SpinnerLidar data uploaded to the Department of Energy Atmosphere to Electrons Data Archive and Portal was processed, quality controlled and assured to guarantee high data availability with the removal of invalid measurements. A multidimensional approach to processing the SpinnerLidar Doppler spectra was developed based on matching erroneous measurements within the two-dimensional lidar scan with patterns inside the multidimensional lidar Doppler spectra. This method allows image processing techniques to be used to remove regions of the Doppler spectra that are contaminated by hard targets and isolate the velocity field of interest, allowing more accurate line-of-sight velocity measurements and enabling the estimation of the turbulence of the line-of-sight velocities within the probe volume.

BUILDING DETECTION FROM LIDAR DATA USING ENTROPY AND THE K-MEANS CONCEPT

Abstract. Information obtained from LiDAR data processing is considered in a variety of applications, among them urban planning. In this context, buildings play a substantial role, since a high percentage of the urban landscape is occupied by them. In the literature, many methodologies have been developed aiming at the detection of building using remote sensing data. The approaches can be developed by applying different ideas: regularity of cluster boundary, plane fitting, radiometric data and also in geometric attribute derived from LiDAR. This paper proposes a method of building detection based on the use of the entropy concept and the K-means algorithm in which the training step is dispensed with. The experiments were performed considering two LiDAR datasets with different densities (12.5 pts/m2 and 4 pts/m2). Visual and qualitative analysis enabled verification of the potential of the proposed method, which presented satisfactory results for both datasets.

AUTOMATIC DETECTION OF FOREST-ROAD DISTANCES TO IMPROVE CLEARING OPERATIONS IN ROAD MANAGEMENT

Abstract. There is a complex relation between roads and fires. Several major wildfires were ignited near to roads (Morrison 2007) and how they progressed is an important role to understand the importance to forest management in this environment. Nowadays, a sustainable forest management is necessary both for environment and politics. One of the reasons of road management is that these infrastructures provide an effective firewall in case of forest fires and an escape route for the population. Forest management optimization in road surroundings would improve wildfires prevention and mitigate their effects. One of the main indicators of road safety is the distance between road and vegetation.The aim of this work is to develop a methodology to determine what areas do not obey current laws about safety distances between forest and roads. The acquisition of LiDAR data is done by Lynx Mobile Mapper System from University of Vigo. The methodology is automated using LiDAR data processing. The developed algorithms are based in height and length segmentation of the road. The objective is classifying vegetation groups by height and calculate the distance to the edges of road. The vegetation is divided in groups of height of 5, 10, 15 and 30 m. The minimum distance calculation is 2 m, for the vegetation of 5 m height and a maximum of 60 m for vegetation 30 m height. The height of vegetation has a directly relation with the distance separation with the road.

DESCRIBING THE VERTICAL STRUCTURE OF INFORMAL SETTLEMENTS ON THE BASIS OF LIDAR DATA – A CASE STUDY FOR FAVELAS (SLUMS) IN SAO PAULO CITY

Abstract. Cadastral mapping of favela’s agglomerated buildings in informal settlements at Level of Detail 1 (LoD1) usually requires specific surveys and extensive manual data processing. Therefore, there is a demand for including the favelas in the city map production on the basis of Lidar surveys, as well as the detection of their vertical growth. However, the currently developed algorithms for automatically extracting buildings from airborne Lidar data have mainly been tested only for regular building reconstruction. This study aims to develop a Lidar data processing pipeline enabling to compute metrics related to intraurban informal settlements. To do so, we present a procedure to generate favela’s buildings delineation, height, floors’ number and built area and apply them to six case studies in favela typo-morphologies. We conducted an exploratory analysis in order to obtain the adequate parameters of the processing pipeline and its evaluation, using open source, free license and self-developed software. The results are compared to reference data from the manual stereo plotting, achieving a quality index in the building reconstruction about 70%. We also calculated the growth density, measured by gross Floor Area Ratio index inside settlement, revealing values from 29% to 74% considering different time periods.

Editorial for the Special Issue “Remote Sensing of Atmospheric Conditions for Wind Energy Applications”

This Special Issue hosts papers on aspects of remote sensing for atmospheric conditions for wind energy applications. The wind lidar technology is presented from a theoretical view on the coherent focused Doppler lidar principles. Furthermore, wind lidar for applied use for wind turbine control, wind farm wake, and gust characterizations are presented, as well as methods to reduce uncertainty when using lidar in complex terrain. Wind lidar observations are used to validate numerical model results. Wind Doppler lidar mounted on aircraft used for observing winds in hurricane conditions and Doppler radar on the ground used for very short-term wind forecasting are presented. For the offshore environment, floating lidar data processing is presented as well as an experiment with wind-profiling lidar on a ferry for model validation. Assessments of wind resources in the coastal zone using wind-profiling lidar and global wind maps using satellite data are presented.

Point Cloud Processing System Development Based on PCL and Qt

LiDAR technology has been widely applied in various disciplines as it can obtain 3D information of targets directly and accurately. However, it is still a challenge to processing LiDAR point clouds efficiently as its huge datasets and complicated processing procedures. Current processing methods need integrate multiple software to complete the whole processing procedures to produce final results which needs lots of time effort and cause low efficiency. By analyzing the theories and methods of LiDAR data processing procedures, this research aims to develop a new point cloud processing software based on PCL and Qt. Firstly, the overall design and modules of the processing system was introduced. The main modules include data management, visualization, filtering, segmentation modeling and auxiliary function. Secondly, to improve system security and maintenance convenience, the system adopts the object-oriented programming method to encapsulate private members and methods of classes, and only open public member variables and methods are available to users. The main classes which were employed in this research were explained. Finally, indoor environments datasets were used to verify the point cloud processing system. The results showed that system has strong interactivity, intuitive display, easy to use and comprehensive features and good results can be derived.

LiDAR-Enhanced Connected Infrastructures Sensing and Broadcasting High-Resolution Traffic Information Serving Smart Cities

Connected-vehicle system is an important component of smart cities. The complete benefits of connected-vehicle technologies need the real-time information of all vehicles and other road users. However, the existing connected-vehicle deployments obtain the real-time status of connected vehicles, but without knowing the unconnected traffic since there are still many unconnected vehicles and pedestrians on the roads. Therefore, it is urgent to find an approach to collect the high-resolution real-time status of unconnected road users. When it is difficult for all vehicles, pedestrians, and bicyclists to broadcast their real-time status in the near future, enhancing the traffic infrastructures to actively sense and broadcast each road user's status is an intuitive solution to fill the data gap. This paper introduces a new-generation LiDAR-enhanced connected infrastructures that can actively sense the high-resolution status of surrounding traffic participants with roadside LiDAR sensors and broadcast connected-vehicle messages through DSRC roadside units. The system architecture, the LiDAR data processing procedure, the data communication, and the first pilot implementation at an intersection in Reno, Nevada are included in this paper. This research is the start of the new-generation connected infrastructures serving connected/autonomous vehicles with the roadside LiDAR sensors. It will accelerate the deployment of the connected network for the smart cities to improve traffic safety, mobility, and fuel efficiency.

Application of Chebyshev approaching function in a direct-detection wind Doppler Lidar data processing

Data retrieval algorithms for direct-detection Doppler wind Lidar still need further studies due to their great impact on filtering out noises introduced wind fluctuations for numerical prediction application. In this work, a technique based on Chebyshev approaching function for processing Doppler wind profiles in frequency domain is presented. After band-pass filtering, the wind profiles with large and small perturbations are clear distinguished. And as an example, the wind profiles obtained by a mobile Doppler Lidar from March 21 to April 18, 2011 in Beijing is compared to the data from radiosondes detection after such processing. The results show good consistency between them and demonstrate the efficient of the algorithm. Furthermore, such data processing algorithm can help to improve mes-micro scale numerical prediction accuracy.

DATA MINING FOR CLASSIFICATION OF HIGH VOLUME DENSE LiDAR DATA IN AN URBAN AREA

Abstract. 3D LiDAR point cloud obtained from the laser scanner is too dense and contains millions of points with information. For such huge volume of data to be sorted, identified, validated and be used for prediction, data mining provides immense scope and has been used to achieve the same. Certain unique attributes were selected as an input for creating models through machine learning. Supervised models were thus built for prediction of classes through the available LiDAR data using random forest algorithm. The algorithm was chosen owing to its efficiency and accuracy over other data mining algorithms. The models created using random forest were then tested on an unclassified point cloud data of an urban area. The method shows promising results in terms of classification accuracy as overall accuracy of 91.71 % was achieved for pixel-based classification. The method also displays enhanced efficiency over common classification algorithms as the time taken to make predictions about the data is reduced considerably for a set of dense LiDAR data. This shows positive foresight of making use of data mining and machine learning to handle large volume of LiDAR data and can go a long way in augmenting efficient processing of LiDAR data.

Systems Engineering for Lidar-Assisted Control: A Sequential Approach

This work presents a sequential approach to explore and optimize the benefits of lidar-assisted control for wind turbines. The optimization is divided in three steps: lidar hardware, lidar data processing, and feedback controller optimization. Appropriate optimization criteria and computational efficient models are used for the intermediate steps and energy production is optimized in the last step with a full aero-elastic model to provide an estimation of lidar-assisted control without the need of a detailed cost model. The case study shows that lidar-assisted control together with an adjustment of the power level are promising to extend the life-time of wind turbines and finally increase the energy capture.

Verification of an Algorithm for Processing Data from Multifrequency Lidar Sensing of Aerosols

We consider an algorithm for processing lidar signals, which for a lidar path with constant qualitative aerosol characteristics (particle size distribution in each aerosol mode, complex refractive index of the particles) lets us obtain a concentration profile for the fine aerosol component without using additional a priori information. The algorithm was verified based on lidar data from the Institute of Physics (National Academy of Sciences of Belarus). The vertical volume concentration profile for fine particles obtained by our algorithm is quite consistent with the results of its independent retrieval, based on integrated processing of lidar and sun photometer data.

Automatic Measurement of Water Height in the As Conchas (Spain) Reservoir Using Sentinel 2 and Aerial LiDAR Data

A methodology for the measurement of height in water reservoirs is developed. It is based on Sentinel 2 imagery and aerial LiDAR data. The methodology is automatized using Matlab software and focused on image processing techniques (equalization, binarization, and edge detection) combined with LiDAR data processing (near neighbour search and height averaging). It is applied in a region of interest selected by the user characterized by a water–land interface. Results are validated in the As Conchas water reservoir (Spain) using an in situ sensing system provided by the Hydrographic Miño-Sil Confederation. The duration of the experiment was one year. The Sentinel 2 bands B2, B3, B4, and B8 were tested during this study. The best results for water height evaluation were obtained for band B8 (842 nm) with an error of 0.20 m and a standard deviation of 0.17 m. The time resolution of the technique depends on the Sentinel 2 revisit time. The time resolution and height accuracy could be improved using complementary satellite systems.

Scientific and Technical Aspects of Applying the Method of Principal Components in the Processing of Lidar Data

Процесс обработки экспериментальных данных с целью последующего принятия решений в области управления мобильным роботом является одним из сложных процессов, требующих применения современных алгоритмов и математических методов. Наиболее часто данные о позиционировании мобильного робота формируются с помощью систем технического зрения. Одной из проблем управления мобильными роботами является своевременное определение координат местоположения мобильного робота и препятствий его движению. Рассматриваемая система технического зрения на основе LIDAR не только осуществляет пространственную ориентацию мобильного робота, но и формирует единый технический модуль. Указанный модуль обладает рядом преимуществ при обработке экспериментальных данных производственных помещений, имеющих значительную площадь и представленных множеством динамических и статических объектов. В работе предложено использование алгоритма агломеративной иерархической кластеризации с применением метода главных компонент для обработки данных о местоположении робота, полученных при помощи лазерного дальномера Hokuyo UTM-30LN. Статья посвящена разработке системы сканирования и последующего формирования маршрутной карты с использованием двух параметров системы технического зрения - расстояния и интенсивности. В работе приведено математическое описание алгоритма кластеризации, реализован процесс построения маршрутной карты мобильным роботом с использованием данных, полученных при помощи LIDAR-системы. Указаны результаты обработки действительных данных, доказывающих эффективность применения модифицированного алгоритма с использованием LIDAR-параметров: расстояния до объекта и интенсивности светового излучения.

Impact of varying lidar measurement and data processing techniques in evaluating cirrus cloud and aerosol direct radiative effects

Abstract. In the past 2 decades, ground-based lidar networks have drastically increased in scope and relevance, thanks primarily to the advent of lidar observations from space and their need for validation. Lidar observations of aerosol and cloud geometrical, optical and microphysical atmospheric properties are subsequently used to evaluate their direct radiative effects on climate. However, the retrievals are strongly dependent on the lidar instrument measurement technique and subsequent data processing methodologies. In this paper, we evaluate the discrepancies between the use of Raman and elastic lidar measurement techniques and corresponding data processing methods for two aerosol layers in the free troposphere and for two cirrus clouds with different optical depths. Results show that the different lidar techniques are responsible for discrepancies in the model-derived direct radiative effects for biomass burning (0.05 W m−2 at surface and 0.007 W m−2 at top of the atmosphere) and dust aerosol layers (0.7 W m−2 at surface and 0.85 W m−2 at top of the atmosphere). Data processing is further responsible for discrepancies in both thin (0.55 W m−2 at surface and 2.7 W m−2 at top of the atmosphere) and opaque (7.7 W m−2 at surface and 11.8 W m−2 at top of the atmosphere) cirrus clouds. Direct radiative effect discrepancies can be attributed to the larger variability of the lidar ratio for aerosols (20–150 sr) than for clouds (20–35 sr). For this reason, the influence of the applied lidar technique plays a more fundamental role in aerosol monitoring because the lidar ratio must be retrieved with relatively high accuracy. In contrast, for cirrus clouds, with the lidar ratio being much less variable, the data processing is critical because smoothing it modifies the aerosol and cloud vertically resolved extinction profile that is used as input to compute direct radiative effect calculations.

Investigating the performances of commercial and non-commercial software for ground filtering of UAV-based point clouds

ABSTRACTIn recent years, the advent of unmanned aerial vehicles (UAV)-based photogrammetry has enabled the collection of accurate and comprehensive information from the surface of the Earth. Owing to low-altitude flights, it is possible to generate high-density point clouds, which are useful for accurate representation of topography of the land surface. Ground filtering is the removal of the points belonging to above-ground objects in order to retrieve ground points to be used in generating digital terrain models. It is essential in most applications for modelling the environment and is performed by using various types of commercial and non-commercial software. This study investigates the performances of seven widely used ground filtering algorithms on UAV-based point clouds. These algorithms are (1) the adaptive triangulated irregular network implemented into the commercial Agisoft Photoscan Professional software, (2) the multi-scale curvature classification implemented into the commercial global mapper software, (3) the cloth simulation filtering (CSF) applied with a MATLAB script, (4) the interpolation-based Boise Centre Aerospace Laboratory-lidar algorithm embedded in the commercial environment for visualizing images software, (5) the interpolation-based gLiDAR non-commercial software, (6) the 2D progressive morphological algorithms, and (7) elevation threshold with expand window algorithms embedded in the non-commercial airborne lidar data processing and analysis tools software. The results showed that the CSF algorithm presented the best filtering results for both test sites with Total Errors of 6% and 4.5% in the sites 1 and 2, respectively.

Geoinformation approach to the urban geographic system research (case studies of Kharkiv region)

Research problem introduction. The main research goal of this paper is to provide the urban geosystem research concept with both the theoretical basics presentation of GIS involvement in urban studies, and with examples of its practical applications. An urbogeosystem (UGS) has been presented not as a simple aggregate of cities, but as the emergent entity that produced complicated interconnections and interdependencies among its constituents. By the urbogeosystem concept the authors attempt to introduce a reliable research approach that has been deliberately developed to identify the nature and spatial peculiarities of the urbanization process in a given area. The exigency of this concept elaboration is listed by the number of needs and illustrated with ordinary 2D digital city cadaster limitations. The methodological background has been proposed, and its derivative applied solutions meet the number of necessities for more efficient urban mapping, city understanding, and municipal mana-gement. The geoinformation concept of the urban geographic system research. External and internal urbogeosystems. The authors explain why an UGS can be formalized as three major components: an aggregate of point features, a set of lines, an aggregate of areal features. The external UGS represents a set of cities, the internal one – a set of delineated areas within one urban territory. Algorithmic sequence of the urbogeosystem study with a GIS. The authors introduce algorithmic sequence of research provision with GIS, in which the LiDAR data processing block has been examined in the details with the procedure of the automated feature extraction explanation. Relevant software user interface sample of the visualization of the urban modeled feature attributes is provided. A case study of the external urbogeosystem. The regional case study of the external urbogeosystem modeling is introduced with GIS MapInfo Professional. The authors present the spatial econometric analysis for commuting study directed to a regional workforce market. The results of the external UGS research mainly correspond to some published social economic regularities in the area, but nonetheless it also demonstrates significant deviations that may be explained by this system’s emergent properties. Case studies of the internal urbogeosystem of Kharkiv-City. Two case studies of the internal urbogeosystem of Kharkiv City have been demonstrated, too. In the first one, automated feature extraction provided by the authors’ original software from LiDAR data has been applied for modeling this UGS content throughout a densely built-up urban parcel. In another case the GIS-analysis of the urbogeosystem functional impact on the catering services spatial distribution has been provided with the ArcGIS software. Results and conclusion. Summarizing all primary and derivative data processed with this technique as well as generalizing key ideas discussed in the text, the authors underline this whole methodological approach as such that can be considered as a general outlining showing how to use geoinformation software for the analysis of urban areas. Concluding their research, the authors emphasize that the urbogeosystem concept may be quite useful for visualization and different analysis applied for urban areas, including city planning, facility and other municipal management methods. The short list of the obtained results has been provided at the end of the text.

Development of a Self-localization Technology with LiDAR Data Processing Utilizing Landmark Information

Development of a Self-localization Technology with LiDAR Data Processing Utilizing Landmark Information