A Filter Method for Vehicle-Based Moving LiDAR Point Cloud Data for Removing IRI-Insensitive Components of Longitudinal Profile.
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| Title: | A Filter Method for Vehicle-Based Moving LiDAR Point Cloud Data for Removing IRI-Insensitive Components of Longitudinal Profile. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18020240 |
