scholarly journals IMPACT OF MOBILE LASER SCANNING DATA PRELIMINARY PROCESSING RESULTS ON ACCURACY OF GENERATING DIGITAL MODELS OF ROAD PAVEMENT

2020 ◽  
Vol 1 (1) ◽  
pp. 74-85
Author(s):  
Maxim A. Altyntsev ◽  
Karkokli Hamid Saber
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Road Surface ◽  
Surface Model ◽  
Technical Equipment ◽  
Digital Cameras ◽  
The Road ◽  
Road Pavement ◽  
Short Period ◽  
Processing Techniques

Various technical equipment is used for surveying the condition of the road surface. One of the most modern methods for assessing the state of roads is mobile laser scanning (MLS), which allows obtaining a point model of road surface with high density accuracy and within a short period of time. After generating a digital surface model (DSM) from an array of laser points, we can identify various defects of the roadway and evaluate its flatness. A prerequisite for obtaining reliable survey results is increased accuracy of the pre-processed point cloud. During the pre-processing 2D scanners and digital cameras are calibrated, point clouds are adjusted and filtered. The necessity for increased accuracy of pre-processing results for the purpose of generating the DSM imposes certain requirements on data processing techniques. For this purpose, additional study of the MLS data accuracy should be often carried out. The results of preliminary processed MLS data in order to generate high-accuracy DSM of road pavement are discussed.

scholarly journals APPLICATION OF TERRESTRIAL LASER SCANNING FOR ASSESSMENT OF ASPHALT PAVEMENT LAYING QUALITY

2021 ◽  
Vol 1 ◽  
pp. 75-84
Author(s):  
Maxim A. Altyntsev ◽  
Marina A. Altyntseva
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Road Surface ◽  
Surface Model ◽  
Technical Equipment ◽  
Digital Cameras ◽  
The Road ◽  
Road Pavement ◽  
Short Period ◽  
Processing Techniques

Various technical equipment are used for surveying the condition of the road surface. One of the most modern methods for assessing the state of roads is mobile laser scanning (MLS), which allows obtaining a point model of road surface with high density accuracy and in a short period of time. After generating a digital surface model (DSM) from an array of laser points, we can identify various defects of the roadway and evaluate its flatness. A prerequisite for obtaining reliable survey results is increased accuracy of the pre-processed point cloud. During pre-processing 2D scanners and digital cameras are calibrated, point clouds are adjusted and filtered. The necessity for increased accuracy of pre-processing results for the purpose of generating the DSM imposes certain requirements on data processing techniques. For this purpose, additional study of the MLS data accuracy should often be carried out. The results of preliminary processed MLS data in order to generate high-accuracy DSM of road pavement are discussed.

scholarly journals Mobile Laser Scanning Data for the Evaluation of Pavement Surface Distress

2020 ◽  
Vol 12 (6) ◽  
pp. 942 ◽  
Author(s):  
Maria Rosaria De Blasiis ◽  
Alessandro Di Benedetto ◽  
Margherita Fiani
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Road Traffic ◽  
Single Point ◽  
Three Dimensional ◽  
Point Clouds ◽  
Road Surface ◽  
Important Indicator ◽  
The Road ◽  
Laser Systems

The surface conditions of road pavements, including the occurrence and severity of distresses present on the surface, are an important indicator of pavement performance. Periodic monitoring and condition assessment is an essential requirement for the safety of vehicles moving on that road and the wellbeing of people. The traditional characterization of the different types of distress often involves complex activities, sometimes inefficient and risky, as they interfere with road traffic. The mobile laser systems (MLS) are now widely used to acquire detailed information about the road surface in terms of a three-dimensional point cloud. Despite its increasing use, there are still no standards for the acquisition and processing of the data collected. The aim of our work was to develop a procedure for processing the data acquired by MLS, in order to identify the localized degradations that mostly affect safety. We have studied the data flow and implemented several processing algorithms to identify and quantify a few types of distresses, namely potholes and swells/shoves, starting from very dense point clouds. We have implemented data processing in four steps: (i) editing of the point cloud to extract only the points belonging to the road surface, (ii) determination of the road roughness as deviation in height of every single point of the cloud with respect to the modeled road surface, (iii) segmentation of the distress (iv) computation of the main geometric parameters of the distress in order to classify it by severity levels. The results obtained by the proposed methodology are promising. The procedures implemented have made it possible to correctly segmented and identify the types of distress to be analyzed, in accordance with the on-site inspections. The tests carried out have shown that the choice of the values of some parameters to give as input to the software is not trivial: the choice of some of them is based on considerations related to the nature of the data, for others, it derives from the distress to be segmented. Due to the different possible configurations of the various distresses it is better to choose these parameters according to the boundary conditions and not to impose default values. The test involved a 100-m long urban road segment, the surface of which was measured with an MLS installed on a vehicle that traveled the road at 10 km/h.

scholarly journals Automated Method for Detection of Missing Road Point Regions in Mobile Laser Scanning Data

2019 ◽  
Vol 8 (12) ◽  
pp. 525 ◽  
Author(s):  
Yang Ma ◽  
Yubing Zheng ◽  
Said Easa ◽  
Mingyu Hou ◽  
Jianchuan Cheng
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Information Loss ◽  
Substantial Influence ◽  
Pixel Size ◽  
Image Processing Techniques ◽  
The Road ◽  
Automated Method ◽  
Vehicle Path ◽  
Processing Techniques

The paper proposes a method supported by MATLAB for detection and measurement of missing point regions (MPR) which may cause severe road information loss in mobile laser scanning (MLS) point clouds. First, the scan-angle thresholds are used to segment the road area for MPR detection. Second, the segmented part is mapped onto a binary image with a pixel size of ε through rasterization. Then, MPR featuring connected 1-pixels are identified and measured via image processing techniques. Finally, the parameters regarding MPR in the image space are reparametrized in relation to the vehicle path recorded in MLS data for a better understanding of MPR properties on the geodetic plane. Tests on two MLS datasets show that the output of the proposed approach can effectively detect and assess MPR in the dataset. The ε parameter exerts a substantial influence on the performance of the method, and it is recommended that its value should be optimized for accurate MPR detections.

scholarly journals Process Evaluation for Smart Concrete Road Construction:Road Surface and Thickness Evaluation Using High-Speed LiDAR Technology

Automation ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 31-47
Author(s):  
Patric Skalecki ◽  
Maximilian Sesselmann ◽  
Sabrina Rechkemmer ◽  
Thorsten Britz ◽  
Andreas Großmann ◽  
...  
Keyword(s):  
Layer Thickness ◽  
High Speed ◽  
Laser Scanning ◽  
Classical Method ◽  
Early Stage ◽  
Quality Criteria ◽  
Point Clouds ◽  
Base Layer ◽  
Surface Model ◽  
The Road

The enhancement of new quality criteria in highway construction is a key aspect to improving the construction process and lifetime of road. In particular, mobile laser scanning systems are nowadays able to provide realistic 3D elevation profiles of a road to detect anomalies. In this context, this study utilizes a high-accuracy high-speed mobile mapping vehicle and evaluates a weighted longitudinal profile as an improved measure for evenness analysis. For comparison a classical method with a rolling straight edge was evaluated on the same road section and observed effects are discussed. The second focus is the areal reconstruction of the road thickness. For this purpose, a modern method was developed to spatially synchronize two high-speed laser scans using reference boxes next to the road, to transfer the point clouds into a surface model and to calculate the layer thickness. This procedure was conceptually validated by some pointwise measurements of the layer thickness. With this information, imperfections in the base layer could be detected automatically over a wide area at an early stage and countermeasures might be initiated before constructing the highway.

scholarly journals AUTOMATIC EXTRACTION OF ROAD MARKINGS FROM MOBILE LASER SCANNING DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 825-830 ◽  
Author(s):  
H. Ma ◽  
Z. Pei ◽  
Z. Wei ◽  
R. Zhong
Keyword(s):  
Template Matching ◽  
Laser Scanning ◽  
Point Clouds ◽  
Road Surface ◽  
Trajectory Data ◽  
Critical Feature ◽  
The Road ◽  
Survey Costs ◽  
3D Information ◽  
Seed Points

Road markings as critical feature in high-defination maps, which are Advanced Driver Assistance System (ADAS) and self-driving technology required, have important functions in providing guidance and information to moving cars. Mobile laser scanning (MLS) system is an effective way to obtain the 3D information of the road surface, including road markings, at highway speeds and at less than traditional survey costs. This paper presents a novel method to automatically extract road markings from MLS point clouds. Ground points are first filtered from raw input point clouds using neighborhood elevation consistency method. The basic assumption of the method is that the road surface is smooth. Points with small elevation-difference between neighborhood are considered to be ground points. Then ground points are partitioned into a set of profiles according to trajectory data. The intensity histogram of points in each profile is generated to find intensity jumps in certain threshold which inversely to laser distance. The separated points are used as seed points to region grow based on intensity so as to obtain road mark of integrity. We use the point cloud template-matching method to refine the road marking candidates via removing the noise clusters with low correlation coefficient. During experiment with a MLS point set of about 2 kilometres in a city center, our method provides a promising solution to the road markings extraction from MLS data.

scholarly journals MULTISPECTRAL AIRBORNE LASER SCANNING FOR AUTOMATED MAP UPDATING

2016 ◽  
Vol XLI-B3 ◽  
pp. 323-330 ◽  
Author(s):  
Leena Matikainen ◽  
Juha Hyyppä ◽  
Paula Litkey
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Sensor Technology ◽  
Classification Error ◽  
Surface Model ◽  
Airborne Laser ◽  
Map Updating

During the last 20 years, airborne laser scanning (ALS), often combined with multispectral information from aerial images, has shown its high feasibility for automated mapping processes. Recently, the first multispectral airborne laser scanners have been launched, and multispectral information is for the first time directly available for 3D ALS point clouds. This article discusses the potential of this new single-sensor technology in map updating, especially in automated object detection and change detection. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from a random forests analysis suggest that the multispectral intensity information is useful for land cover classification, also when considering ground surface objects and classes, such as roads. An out-of-bag estimate for classification error was about 3% for separating classes asphalt, gravel, rocky areas and low vegetation from each other. For buildings and trees, it was under 1%. According to feature importance analyses, multispectral features based on several channels were more useful that those based on one channel. Automatic change detection utilizing the new multispectral ALS data, an old digital surface model (DSM) and old building vectors was also demonstrated. Overall, our first analyses suggest that the new data are very promising for further increasing the automation level in mapping. The multispectral ALS technology is independent of external illumination conditions, and intensity images produced from the data do not include shadows. These are significant advantages when the development of automated classification and change detection procedures is considered.

Decision Analysis of Lane Change Based on Fuzzy Logic

2013 ◽  
Vol 419 ◽  
pp. 790-794 ◽  
Author(s):  
Wen Shi ◽  
Ya Ping Zhang
Keyword(s):  
Fuzzy Logic ◽  
Change Process ◽  
Road Surface ◽  
Driver Model ◽  
Steering Wheel ◽  
Surface Model ◽  
Lane Change ◽  
The Road ◽  
Fuzzy Logic Analysis ◽  
Test Result

Aiming at the complexity of lane change process, fuzzy logic analysis method was proposed to analyzing this behavior. By assaying the multi lane change scene that the drivers may choose, influencing factors were quantified. Each indicator factor after quantified was treated as model input. PID models of driver, vehicle and road surface were established in Simulink condition. The road surface model controls whether the lane change process will be conducted, and the driver model will export angle of steering wheel to deciding the efficiency of lane change process. Real road test was conducted and the test result shows that information between human and vehicle can be fused sufficiently.

scholarly journals POTENTIAL OF FULL WAVEFORM AIRBORNE LASER SCANNING DATA FOR URBAN AREA CLASSIFICATION – TRANSFER OF CLASSIFICATION APPROACHES BETWEEN MISSIONS

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1317-1323 ◽  
Author(s):  
G. Tran ◽  
D. Nguyen ◽  
M. Milenkovic ◽  
N. Pfeifer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Surface Model ◽  
Knowledge Based ◽  
Full Waveform ◽  
Airborne Laser ◽  
Point Density ◽  
Area Classification

Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.

scholarly journals TINJAUAN KONDISI PERKERASAN JALAN DENGAN KOMBINASI NILAI INTERNATIONAL ROUGHNESS INDEX (IRI) DAN SURFACE DISTRESS INDEX (SDI) PADA JALAN TAKENGON – BLANGKEJEREN

2018 ◽  
Vol 1 (3) ◽  
pp. 543-552
Author(s):  
Baihaqi Baihaqi ◽  
Sofyan M. Saleh ◽  
Renni Anggraini
Keyword(s):  
Road Surface ◽  
Road Segment ◽  
International Roughness Index ◽  
Damage Level ◽  
The Road ◽  
Road Pavement ◽  
Roughness Index ◽  
Surface Distress ◽  
Observation Method ◽  
Pavement Damage

Abstract: Takengon - Blangkejeren road is one of the cross national roads connecting Central Aceh Regency with Gayo Lues Regency. This road is in the mountainous terrain and often passed by heavy loaded vehicles so that often damaged. To overcome the frequent damage to this road segment, it is necessary to conduct a research on road pavement damage. The purpose of this research is to know the condition of road damage based on the combination of International Roughness Index (IRI) and Surface Distress Index (SDI). This study uses direct observation method in the field by conducting a visual survey of road pavement conditions. The result of the research shows that the total damage level of road surface is 30,54% while the road surface is not damaged by 69,46% from total of road that become research object, that is 12,63 Km divided into 6 road segment. For the overall condition of roads reviewed 45.02% good, 45.81% medium, 6.87% lightly damaged, 2.29% heavily damaged.Abstrak: Ruas jalan Takengon – Blangkejeren merupakan salah satu ruas jalan nasional lintas tengah yang menghubungkan Kabupaten Aceh Tengah dengan Kabupaten Gayo Lues. Jalan ini berada pada medan pegunungan dan sering dilalui kendaraan dengan beban yang berat sehingga sering mengalami kerusakan. Untuk mengatasi kerusakan yang sering terjadi pada ruas jalan ini perlu diadakan suatu penelitian mengenai jenis kerusakan perkerasan jalan. Tujuan dari penelitian ini adalah untuk mengetahui kondisi kerusakan jalan berdasarkan kombinasi nilai International Roughness Index (IRI) dan Surface Distress Index (SDI). Penelitian ini menggunakan metode pengamatan langsung dilapangan dengan melakukan survey secara visual terhadap kondisi perkerasan jalan. Dari hasil penelitian diperoleh tingkat kerusakan keseluruhan permukaan jalan adalah sebesar 30,54% sedangkan permukaan jalan yang tidak mengalami kerusakan sebesar 69,46 % dari total panjang jalan yang menjadi objek penelitian, yaitu 12,63 Km yang dibagi menjadi 6 buah segmen jalan. Untuk kondisi keseluruhan jalan yang ditinjau 45,02 % baik, 45,81 % sedang, 6,87 % rusak ringan, 2,29 % rusak berat.

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