2021, 13(3): 51-57. doi: 10.16670/j.cnki.cn11-5823/tu.2021.03.08
基于无人机实景建模的机场道路路基平整度检测与分析研究
| 1. | 华中科技大学 土木与水利工程学院，武汉 430074 |
| 2. | 武汉市市政建设集团有限公司，武汉 430056 |
Research on Detection and Analysis of Airport Road Subgrade Smoothness Based on UAV Real Scene Modeling
| 1. | School of Civil & Hydraulic Engineering, Huazhong University of Science & Technology, Wuhan 430074, China |
| 2. | Wuhan Municipal Construction Group Co., Ltd., Wuhan 430056, China |
引用本文:
刘颖, 张勇, 卢吉, 周诚. 基于无人机实景建模的机场道路路基平整度检测与分析研究[J]. 土木建筑工程信息技术,
2021, 13(3): 51-57.
doi: 10.16670/j.cnki.cn11-5823/tu.2021.03.08
Citation:
Ying Liu, Yong Zhang, Ji Lu, Cheng Zhou. Research on Detection and Analysis of Airport Road Subgrade Smoothness Based on UAV Real Scene Modeling[J]. Journal of Information Technologyin Civil Engineering and Architecture,
2021, 13(3): 51-57.
doi: 10.16670/j.cnki.cn11-5823/tu.2021.03.08
摘要:传统机场路基平整度检测存在检测时间长、误差大以及检测结果片面性等问题，平整度难以得到有效评价。为实现机场路基平整度快速、全面、准确的检测，本文提出利用无人机扫描路基图像信息，通过实景建模得到三维实景模型并进行国际平整度指数(IRI)计算的方法进行路基平整度检测。通过某机场滑行道路基施工为例，比较车载激光平整度仪与无人机实景建模的平整度检测结果，结果表明利用无人机实景建模进行平整度检测是可行的，并且该方法能够快速准确地实现全长任意路段的平整度检测，不再局限于行驶车道直线上的平整度检测，避免了片面性和偶然性。
Abstract: The traditional airport subgrade flatness detection has the problems of long detection time, large errors and one-sidedness of detection results, which make it difficult to get an effective evaluation. In order to realize the rapid, comprehensive and accurate detection of airport subgrade evenness, this paper proposes a method to detect subgrade evenness by using UAV scanning subgrade image information, obtaining a three-dimensional real model through real scene modeling and calculating international evenness index (IRI). Taking the foundation construction of an airport taxiway as an example, the flatness detection results of vehicle-mounted laser flatness meter and UAV real scene modeling are compared. The results show that it is feasible to use UAV real scene modeling for flatness detection, and this method can realize the flatness detection of any full-length road section quickly and accurately, and it is no longer limited to the flatness detection on the straight line of the driving lane, avoiding one-sidedness and contingency.
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