Geometric validation of a ground-based mobile laser scanning system

Barber, David M.; Mills, J. P.; Smith-Voysey, Sarah

doi:10.1016/j.isprsjprs.2007.07.005

Cited by 186 publications

(134 citation statements)

References 11 publications

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“…Mobile mapping refers to a methodology of collecting geospatial data using mapping and navigation sensors that are mounted rigidly onboard a mobile platform (Schwarz and El-sheimy, 2007;Barber et al, 2008). Multi sensor integrated mapping technology has enabled rapid and cost effective acquisition of georeferenced information about road network environments (Li and Chapman, 2008).…”

Section: Introductionmentioning

confidence: 99%

Automated road markings extraction from mobile laser scanning data

Kumar

McElhinney

Lewis

et al. 2014

International Journal of Applied Earth Observation and Geoinfor

105

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a b s t r a c tRoad markings are used to provide guidance and instruction to road users for safe and comfortable driving. Enabling rapid, cost-effective and comprehensive approaches to the maintenance of route networks can be greatly improved with detailed information about location, dimension and condition of road markings. Mobile Laser Scanning (MLS) systems provide new opportunities in terms of collecting and processing this information. Laser scanning systems enable multiple attributes of the illuminated target to be recorded including intensity data. The recorded intensity data can be used to distinguish the road markings from other road surface elements due to their higher retro-reflective property. In this paper, we present an automated algorithm for extracting road markings from MLS data. We describe a robust and automated way of applying a range dependent thresholding function to the intensity values to extract road markings. We make novel use of binary morphological operations and generic knowledge of the dimensions of road markings to complete their shapes and remove other road surface elements introduced through the use of thresholding. We present a detailed analysis of the most applicable values required for the input parameters involved in our algorithm. We tested our algorithm on different road sections consisting of multiple distinct types of road markings. The successful extraction of these road markings demonstrates the effectiveness of our algorithm.

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Section: Introductionmentioning

confidence: 99%

Automated road markings extraction from mobile laser scanning data

Kumar

McElhinney

Lewis

et al. 2014

International Journal of Applied Earth Observation and Geoinfor

105

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“…where J is known as the cost function and wi, wj are the weights The best estimate V' can be obtained by differentiating equation (1) and equating the derivative to zero, which yields:…”

Section: Algorithm For Holes Fillingmentioning

confidence: 99%

“…A MLS system integrate GPS/INS system and data acquisition sensors on a rigid moving platform for collecting laser scanning point clouds from the surroundings of the mapping system have been developed since the early 1990s. A good overview is presented in Barber's research article [1]. At present, the integrated GPS/INS/LS system is the key component in both MLS systems and ALS systems.…”

Section: Introductionmentioning

confidence: 99%

3D road surface reconstruction based on point clouds data assimilation algorithm

Chen

Lü

2017

MATEC Web Conf.

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Abstract. Urban areas 3D model reconstruction is one of the major fields of application of 3D scanning technologies. In the future, vehicle-based laser scanning, here called mobile laser scanning system, should see considerable use for 3D road environment modelling in urban areas. In this context, one of the main limitations perceived by the mobile laser scanning system is the incompleteness of the sampling. Whenever we scan urban area road environment, the produced sampling usually presents a large number of missing regions. Many algorithmic solutions exist to close those gaps from specific hole filling algorithms to the drastic solution of using water-tight reconstruction methods. In this paper, a method for filling holes of road surface point clouds and generating 3D model of road surface from mobile laser scanning data is developed. The data is classified into road surface, on-road and off-road surface point clouds. Many large holes in the road surface point clouds are filled by using data assimilation algorithm. Then, the road surface is 3D modeled as a triangulated irregular network. It is shown that the whole road surface 3D model is integrated after data processing. The above mentioned steps are applied to a large set of mobile laser scanning data of urban area road environment, in order to obtain the whole urban road surface 3D model.

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“…Global accuracy decreases as the distance from the differential GPS base station increases, at a maximum rate of 0.5 cm per 1 km with an initial error of 5 mm; this is calculated at 0.5 cm+5 ppm (Glennie, 2007b). A general but thorough overview of accuracy considerations for mobile terrestrial mapping was conducted and published by Barber et al (2008). Global accuracy does not affect the viewability or the analysis of the data, only the relation of the identified features to their true locations in space, and so to other GIS datasets.…”

Section: Mobile Terrestrial Lidarmentioning

confidence: 99%

Engineering monitoring of rockfall hazards along transportation corridors: using mobile terrestrial LiDAR

Lato

Hutchinson

Diederichs

et al. 2009

Nat. Hazards Earth Syst. Sci.

109

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Abstract. Geotechnical hazards along linear transportation corridors are challenging to identify and often require constant monitoring. Inspecting corridors using traditional, manual methods requires the engineer to be unnecessarily exposed to the hazard. It also requires closure of the corridor to ensure safety of the worker from passing vehicles. This paper identifies the use of mobile terrestrial LiDAR data as a compliment to traditional field methods. Mobile terrestrial LiDAR is an emerging remote data collection technique capable of generating accurate fully three-dimensional virtual models while driving at speeds up to 100 km/h. Data is collected from a truck that causes no delays to active traffic nor does it impede corridor use. These resultant georeferenced data can be used for geomechanical structural feature identification and kinematic analysis, rockfall path identification and differential monitoring of rock movement or failure over time. Comparisons between mobile terrestrial and static LiDAR data collection and analysis are presented. As well, detailed discussions on workflow procedures for possible implementation are discussed. Future use of mobile terrestrial LiDAR data for corridor analysis will focus on repeated surveys and developing dynamic four-dimensional models, higher resolution data collection. As well, computationally advanced, spatially accurate, geomechanically controlled three-dimensional rockfall simulations should be investigated.Correspondence to: M. Lato

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Geometric validation of a ground-based mobile laser scanning system

Cited by 186 publications

References 11 publications

Automated road markings extraction from mobile laser scanning data

Automated road markings extraction from mobile laser scanning data

3D road surface reconstruction based on point clouds data assimilation algorithm

Engineering monitoring of rockfall hazards along transportation corridors: using mobile terrestrial LiDAR

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