View in EDS HTML Full Text PDF Full Text

A Filter Method for Vehicle-Based Moving LiDAR Point Cloud Data for Removing IRI-Insensitive Components of Longitudinal Profile.

Saved in:
Bibliographic Details
Title: A Filter Method for Vehicle-Based Moving LiDAR Point Cloud Data for Removing IRI-Insensitive Components of Longitudinal Profile.
Authors: Zhou, Guoqing1 (AUTHOR), Gao, Hanwen2 (AUTHOR), Cai, Yufu1,3 (AUTHOR), Guo, Jiahao1 (AUTHOR), Zhao, Xuesong2,3 (AUTHOR) kjcgzhb01@gxzrzyygy.org.cn
Source: Remote Sensing. Jan2026, Vol. 18 Issue 2, p240. 30p.
Subjects: LIDAR, Pavement testing, Information filtering, Mathematical optimization, Surface roughness, Noise measurement, Statistical smoothing
Abstract: Highlights: What are the main findings? A highly accurate and robust Gaussian filter that adapts to any data source. The method outperforms both traditional filters and deep learning approaches. What are the implications of the main findings? Enables real-time, precise road assessment for cost-effective maintenance. Provides a practical and reliable solution that is ready for immediate industry deployment. The International Roughness Index (IRI) is calculated from elevation profiles acquired by high-speed profilers or laser scanners, but these raw data often contain measurement noise and extraneous wavelength components that can degrade the accuracy of IRI calculations. Existing filtering methods expose a limitation in removing IRI-insensitive wavelength components. Thus, this paper proposes a Gaussian filtering algorithm based on the Nyquist sampling theorem to remove IRI-insensitive components of the longitudinal profile. The proposed approach first adaptively determines Gaussian template lengths according to sampling intervals, and then incorporates a boundary padding strategy to ensure processing stability. The proposed method enables precise wavelength selection within the IRI-sensitive band of 1.3–29.4 m while maintaining computational efficiency. The method was validated using the Paris–Lille dataset and the U.S. Long-Term Pavement Performance (LTPP) program dataset. The filtered profiles were evaluated by Power Spectral Density (PSD), and IRI values were calculated and compared with those obtained by conventional profile filtering methods. The results show that the proposed method is effective in removing the non-sensitive components of IRI and obtaining highly accurate IRI values. Compared with the standard IRI provided by the LTPP dataset, mean absolute error of the IRI values from the proposed method reaches 0.051 m/km, and mean relative error is less than 4%. These findings indicate that the proposed method improves the reliability of IRI calculation. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 191173525
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Filter Method for Vehicle-Based Moving LiDAR Point Cloud Data for Removing IRI-Insensitive Components of Longitudinal Profile.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Guoqing%22">Zhou, Guoqing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gao%2C+Hanwen%22">Gao, Hanwen</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cai%2C+Yufu%22">Cai, Yufu</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Jiahao%22">Guo, Jiahao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Xuesong%22">Zhao, Xuesong</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> kjcgzhb01@gxzrzyygy.org.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jan2026, Vol. 18 Issue 2, p240. 30p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22LIDAR%22">LIDAR</searchLink><br /><searchLink fieldCode="DE" term="%22Pavement+testing%22">Pavement testing</searchLink><br /><searchLink fieldCode="DE" term="%22Information+filtering%22">Information filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Surface+roughness%22">Surface roughness</searchLink><br /><searchLink fieldCode="DE" term="%22Noise+measurement%22">Noise measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+smoothing%22">Statistical smoothing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? A highly accurate and robust Gaussian filter that adapts to any data source. The method outperforms both traditional filters and deep learning approaches. What are the implications of the main findings? Enables real-time, precise road assessment for cost-effective maintenance. Provides a practical and reliable solution that is ready for immediate industry deployment. The International Roughness Index (IRI) is calculated from elevation profiles acquired by high-speed profilers or laser scanners, but these raw data often contain measurement noise and extraneous wavelength components that can degrade the accuracy of IRI calculations. Existing filtering methods expose a limitation in removing IRI-insensitive wavelength components. Thus, this paper proposes a Gaussian filtering algorithm based on the Nyquist sampling theorem to remove IRI-insensitive components of the longitudinal profile. The proposed approach first adaptively determines Gaussian template lengths according to sampling intervals, and then incorporates a boundary padding strategy to ensure processing stability. The proposed method enables precise wavelength selection within the IRI-sensitive band of 1.3–29.4 m while maintaining computational efficiency. The method was validated using the Paris–Lille dataset and the U.S. Long-Term Pavement Performance (LTPP) program dataset. The filtered profiles were evaluated by Power Spectral Density (PSD), and IRI values were calculated and compared with those obtained by conventional profile filtering methods. The results show that the proposed method is effective in removing the non-sensitive components of IRI and obtaining highly accurate IRI values. Compared with the standard IRI provided by the LTPP dataset, mean absolute error of the IRI values from the proposed method reaches 0.051 m/km, and mean relative error is less than 4%. These findings indicate that the proposed method improves the reliability of IRI calculation. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=191173525
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18020240
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 30
        StartPage: 240
    Subjects:
      – SubjectFull: LIDAR
        Type: general
      – SubjectFull: Pavement testing
        Type: general
      – SubjectFull: Information filtering
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Surface roughness
        Type: general
      – SubjectFull: Noise measurement
        Type: general
      – SubjectFull: Statistical smoothing
        Type: general
    Titles:
      – TitleFull: A Filter Method for Vehicle-Based Moving LiDAR Point Cloud Data for Removing IRI-Insensitive Components of Longitudinal Profile.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Zhou, Guoqing
      – PersonEntity:
          Name:
            NameFull: Gao, Hanwen
      – PersonEntity:
          Name:
            NameFull: Cai, Yufu
      – PersonEntity:
          Name:
            NameFull: Guo, Jiahao
      – PersonEntity:
          Name:
            NameFull: Zhao, Xuesong
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 01
              Text: Jan2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1