Efficient point cloud corrections for mobile monitoring applications using road/rail-side infrastructure

Jing, Hao; Meng, Xiaolin; Slatcher, Neil; Hunter, Graham

doi:10.1080/00396265.2020.1719753

Cited by 8 publications

(8 citation statements)

References 36 publications

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“…Other studies focused on improving the processing quality or the achievable accuracy of the derived point clouds. Specifically, methodologies both for the alignment of the lanes of highways as well as the correction of navigation accuracy and point cloud quality, based on the extraction of feature information, were proposed [106,107]. Regarding railway industry, TLS data managed to collect significant amount of surface information in a short time, while monitoring the progress of the renovation of a railway structure [108].…”

Section: Lidarmentioning

confidence: 99%

“…Infrastructure Type Application [92,93] bridge 3D reconstruction model [94,95] bridge building information modelling/structure health monitoring [96] bridge automated crack assessment in concrete bridges [60] bridge damage detection and analysis [97] bridge measurements of vertical displacements [98] bridge automated bridge component recognition [99] bridge detection of shape deformation [100] bridge monitoring of construction progress [101] road extraction of road edges [102,103] road road curb detection [104] road extract road information [105] road maintenance of road pavements [106,107] road road monitoring [108] railway monitoring of renovation progress [109] railway recognition of railroad assets [110][111][112][113] dam deformation monitoring [114,115] archaeological sites structural deformation monitoring…”

Section: Referencementioning

confidence: 99%

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Review of Remote Sensing Approaches and Soft Computing for Infrastructure Monitoring

et al. 2023

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During the past few decades, remote sensing has been established as an innovative, effective and cost-efficient option for the provision of high-quality information concerning infrastructure to governments or decision makers in order to update their plans and/or take actions towards the mitigation of the infrastructure risk. Meanwhile, climate change has emerged as a serious global challenge and hence there is an urgent need to develop reliable and cost-efficient infrastructure monitoring solutions. In this framework, the current study conducts a comprehensive review concerning the use of different remote-sensing sensors for the monitoring of multiple types of infrastructure including roads and railways, dams, bridges, archaeological sites and buildings. The aim of this contribution is to identify the best practices and processing methodologies for the comprehensive monitoring of critical national infrastructure falling under the research project named “PROION”. In light of this, the review summarizes the wide variety of approaches that have been utilized for the monitoring of infrastructure and are based on the collection of remote-sensing data, acquired using the global navigation satellite system (GNSS), synthetic aperture radar (SAR), light detection and ranging (LiDAR) and unmanned aerial vehicles (UAV) sensors. Moreover, great emphasis is given to the contribution of the state-of-the-art soft computing methods throughout infrastructure monitoring aiming to increase the automation of the procedure. The statistical analysis of the reviewing publications revealed that SARs and LiDARs are the prevalent remote-sensing sensors used in infrastructure monitoring concepts, while regarding the type of infrastructure, research is orientated onto transportation networks (road and railway) and bridges. Added to this, deep learning-, fuzzy logic- and expert-based approaches have gained ground in the field of infrastructure monitoring over the past few years.

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Section: Lidarmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Review of Remote Sensing Approaches and Soft Computing for Infrastructure Monitoring

et al. 2023

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“…Unfortunately, most positioning demands are requested from urban areas, where problems frequently occur [7]. Therefore, correction solutions to improve the quality of erroneous MLS point clouds are needed, and it is essential to ensure that MLS point cloud data are corrected to the same accuracy level in a complex environment [10,11].…”

Section: A Backgroundmentioning

confidence: 99%

“…This approach can improve the trajectory position of the MLS platform and produce better overall accuracy of the corrected MLS point cloud. Local errors can be reduced, especially when errors are inconsistent within a dataset [10]. However, the procedure requires many control points (at least every 50 m), which is labor-intensive and time-consuming because it must be performed manually [9,10,13,14].…”

Section: B Literature Reviewmentioning

confidence: 99%

Self-Constrained Baseline 3-D Correction Approach for Mobile Laser Scanning Point Cloud in Complex Urban Road Environments

Li,

Liu,

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The embedded data are used to compute the 3D position of both the laser center and the impact point of the beam on the target object. In addition, the integration of data sourced from GNSS and IMU makes it possible to identify the target position even when there is little or no satellite visibility [14], even in areas with infrastructure masked by trees and in urban areas.…”

Section: Introductionmentioning

confidence: 99%

A Method for Obtaining a DEM with Curved Abscissa from MLS Data for Linear Infrastructure Survey Design

2022

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The sudden deterioration of the condition of linear infrastructure networks makes road management a complex task. Knowledge of the surface condition of the pavement is a requirement in order to estimate the causes of instabilities, select the appropriate action and identify all those sections that require urgent intervention. The mobile laser scanning (MLS) technique allows for a fast and safe diagnosis, thus making it possible to plan an early intervention program quickly and cost-effectively. This paper describes a methodology implemented with a twofold purpose: (i) the optimal definition, during the design phase, of the input parameters of the MLS survey (velocity of the vehicle and acquisition rate), defined through the study of the relationship between these parameters and the density of the scanned points and, therefore, with the resolution that allows the analysis of a certain type of pavement distress; (ii) the creation of a Digital Elevation Model with a curved abscissa (DEMc), specific for the analysis of road pavements. The field surveys made and the procedure developed allowed the velocity of the MLS to be associated with the resolution of the DEMc, and thus its capability to highlight distresses at different levels of severity. The creation of the road model is semiautomatic; the height value of each single node of the grid is estimated through spatial interpolation algorithms. Starting from experimental data, a few charts were created that relate the density of the point cloud to the variation of the acquisition rate, together with the minimum resolution. Depending on the type of distress analyzed, it is possible to infer the values to be respected of the parameters. In this way, it should be possible to draw up a few guidelines about MLS surveys addressing linear infrastructures focused on the optimization of the survey design, so as to identify strategies that can maximize benefits with the same available budget.

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Efficient point cloud corrections for mobile monitoring applications using road/rail-side infrastructure

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 8 publications

References 36 publications

Review of Remote Sensing Approaches and Soft Computing for Infrastructure Monitoring

Review of Remote Sensing Approaches and Soft Computing for Infrastructure Monitoring

Self-Constrained Baseline 3-D Correction Approach for Mobile Laser Scanning Point Cloud in Complex Urban Road Environments

A Method for Obtaining a DEM with Curved Abscissa from MLS Data for Linear Infrastructure Survey Design

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