Creating OpenCRG Road Surface Model from Terrestrial Laser Scanning Data for Autonomous Vehicles

2018 ◽  
pp. 361-369
Author(s):  
Arpad Barsi ◽  
Vivien Poto ◽  
Viktor Tihanyi
Keyword(s):  
Autonomous Vehicles ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Road Surface ◽  
Surface Model

scholarly journals A History of Laser Scanning, Part 1: Space and Defense Applications

2020 ◽  
Vol 86 (7) ◽  
pp. 419-429
Author(s):  
Adam P. Spring
Keyword(s):  
Literature Review ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Planetary Rovers ◽  
Land Vehicles ◽  
History Of ◽  
Landscape Scales ◽  
Hostile Environments

This article presents the origins and evolution of midrange terrestrial laser scanning (TLS), spanning primarily from the 1950s to the time of publication. Particular attention is given to developments in hardware and software that document the physical dimensions of a scene as a point cloud. These developments include parameters for accuracy, repeatability, and resolution in the midrange—millimeter and centimeter levels when recording objects at building and landscape scales up to a kilometer away. The article is split into two parts: Part one starts with early space and defense applications, and part two examines the survey applications that formed around TLS technologies in the 1990s. The origins of midrange TLS, ironically, begin in space and defense applications, which shaped the development of sensors and information processing via autonomous vehicles. Included are planetary rovers, space shuttles, robots, and land vehicles designed for relative navigation in hostile environments like space and war zones. Key people in the midrange TLS community were consulted throughout the 10-year period over which this article was written. A multilingual and multidisciplinary literature review—comprising media written or produced in Chinese, English, French, German, Japanese, Italian, and Russian—was also an integral part of this research.

scholarly journals ENVIRONMENTAL DATA DELIVERY FOR AUTOMOTIVE SIMULATIONS BY LASER SCANNING

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 41-45
Author(s):  
A. Barsi ◽  
A. Csepinszky ◽  
N. Krausz ◽  
H. Neuberger ◽  
V. Poto ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Laser Scanning ◽  
Point Clouds ◽  
Environmental Data ◽  
Surface Model ◽  
University Campus ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Computer Based ◽  
The University

<p><strong>Abstract.</strong> The development of autonomous vehicles nowadays is attractive, but a resource-intensive procedure. It requires huge time and money efforts. The different carmakers have therefore common struggles of involving cheaper, faster and accurate computer-based tools, among them the simulators. Automotive simulations expect reality information, where the recent data collection techniques have excellent contribution possibilities. Accordingly, the paper has a focus on the use of mobile laser scanning data in supporting automotive simulators. There was created a pilot site around the university campus, which is a road network with very diverse neighborhood. The data acquisition was conducted by a Leica Pegasus Two mobile mapping system. The achieved point clouds and imagery were submitted to extract road axes, road borders, but also lane borders and lane markings. By this evaluation, the OpenDRIVE representation was built, which is directly transferable into various simulators. Based on the roads’ geometric description, a standardized pavement surface model was created in OpenCRG format. CRG is a Curved Regular Grid, containing all surface height information and objects, but also anomalies. The 3D laser point clouds could easily be transformed into voxel models, then these models can be projected onto two vertical roadside grids (ribbons), which are practically an extension to the OpenCRG model. Adequate visualizations demonstrate the obtained results.</p>

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 Biomass and Yield Estimation of MR219 and MR220 of Paddy Varieties using Terrestrial Laser Scanning Data

2021 ◽  
Vol 34 ◽  
pp. 54-62
Author(s):  
Zareen Zulkifli ◽  
Siti Khairunniza Bejo ◽  
Farrah M. Muharam ◽  
Ian Yule ◽  
Reddy Pullanagari ◽  
...  
Keyword(s):  
Grain Yield ◽  
Plant Height ◽  
Laser Scanning ◽  
Oryza Sativa L ◽  
Terrestrial Laser Scanning ◽  
Surface Model ◽  
Estimation Model ◽  
N Level ◽  
High Production ◽  
Main Food Source

Rice (Oryza Sativa L.) is the main food source in Malaysia. Thus, to fulfill the needs, continuous rice production is required. Appropriate amount of nitrogen (N) fertilizer is needed to ensure high production of rice. In this research, the effect of N to plant height, SPAD reading, biomass and yield were firstly studied. It was later followed by the estimation of biomass and yield using Terrestrial Laser Scanning (TLS) data. Different amount of N i.e. 0 kg/ha, 85 kg/ha, 170 kg/ha and 250 kg/ha were applied to MR 219 and MR 220 paddy. The 2-way ANOVA results showed that all parameters were significantly different at each N level. The highest reading was achieved at 250 kg/ha of N level; 70.46 cm (plant height), 39.13 (SPAD reading), 927.29 g/m2 (biomass) and 830.99 g/m2 (grain yield) respectively. Therefore, these parameters can be used to indicate the level of input nitrogen at the plant. Later, the plant height calculated using developed Crop Surface Model (CSM) of the Terrestrial Laser Scanning (TLS) data was used to evaluate the biomass and grain yield of paddy. Results has shown that high correlations and regression were accomplished for CSM plant height and biomass (R2 = 0.809). However, the results between CSM plant height and grain were lower (R2 = 0.582). In accordance with the outcome, biomass and yield were best estimated at 94 Day After Sowing (DAS). An estimation model for biomass and grain yield using linear equation was developed. Then a t-test was done to test the estimated and measured biomass and grain yield. The outcome showed that there was no significance difference between measured and estimated values. The values for both parameters were 1 (p≥0.05). Thus, it can be said that CSM plant height can be used to estimate biomass and grain yield.

Combining UAV LiDAR and Terrestrial Laser Scanning to investigate the impact of shrub expansion on local-scale Arctic snowpack distribution.

2020 ◽  
Author(s):  
Maxim Lamare ◽  
Laurent Arnaud ◽  
Ghislain Picard ◽  
Maude Pelletier ◽  
Florent Domine
Keyword(s):  
Snow Depth ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Snow Accumulation ◽  
Surface Model ◽  
Shrub Expansion ◽  
Vegetation Height ◽  
Digital Terrain ◽  
Dwarf Birch ◽  
The Impact

&lt;p&gt;&lt;span&gt;Climate warming induces shrub expansion on Arctic herb tundra, with effects on snow trapping and hence snow depth. We have used UAV-borne LiDAR and Terrestrial Laser Scanning (TLS) to investigate the impact of shrub height on snow depth at two close sites near Umiujaq, eastern Canadian low Arctic, where dwarf birch and willow shrubs are expanding on lichen tundra. The first site features lichen and high shrubs (50-100 cm), a moderate relief, and a snowpack averaging 95 cm in spring. The second site consists of lichen and low shrubs (20-60 cm), more pronounced topography, and a deeper snowpack (101 cm). Digital Terrain and Surface Models were acquired in early fall to obtain topography and vegetation height. A Digital Surface Model obtained in spring produced snow depth maps at peak depth. TLS over a 400 m&lt;sup&gt;2&lt;/sup&gt; area produced time series of snow depth throughout the winter. TLS data show preferential snow accumulation in shrubs, but also preferential melting in shrubs during fall warm spells and in spring. UAV data at the first site show a strong correlation between vegetation height and snow depth, even after snow depth has exceeded vegetation height. This correlation is not observed at the second site, probably because snow depth there is much greater than vegetation height. These data show the need to reconsider some paradigms on snow-vegetation interactions, for example that vegetation does not affect snow accumulation beyond its height. &lt;/span&gt;&lt;/p&gt;

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 TERRESTRIAL LASER SCANNING ASSESSMENT OF GENERALIZATION ERRORS IN CONVENTIONAL TOPOGRAPHIC SURVEYS

2015 ◽  
Vol 41 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Martin Sobak ◽  
Artu Ellmann ◽  
Tarvo Mill
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Surface Model ◽  
Relative Height ◽  
North East ◽  
Numerical Assessment ◽  
Surface Models ◽  
Generalization Errors ◽  
Topographic Surveys

The technology of terrestrial laser scanning has widely been used in the surveying industry in recent years due to higher data collecting productivity compared to traditional tacheometric survey. The aim of this study is to assess generalization errors in topographic surveys of landforms on the basis of a large vegetation free semi-coke landfill hill with the relative height of 116 m in North-East Estonia. The numerical assessment of errors is proceeded by comparing a high-resolution terrestrial laser scanning (TLS) 3D surface model with surface models generated from the sparser data steps (10, 20, 30 and 50 m). The 10 and 20 m data step surface models yield discrepancies within ± 20 cm. The 30 m data step models revealed slightly larger differences. Expectedly the largest elevation differences reaching up to 2.5 m were associated with the 50 m point step.

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