Light Detection and Ranging (LiDAR), an advanced non-contact sensing method capable of capturing 3D spatial data with up to millimeter-level precision depending on the ranging method, has been widely used in pavement defect detection and road asset management. This paper provides an overview of LiDAR-based pavement inspection techniques in terms of measurement principles, characterization of acquisition methods, and algorithmic processing of point cloud data. Subsequently, the characteristics of major LiDAR systems, including mobile laser scanning (MLS), terrestrial laser scanning (TLS), and airborne laser scanning (ALS), and their applicability for pavement information inspection are analyzed. MLS emerges as the predominant method due to its superior mobility and measurement precision in retrieving pavement data. Then, traditional and deep learning-based 3D point cloud processing algorithms are compared for pavement information inspection, challenges in achieving high accuracy and efficiency with large datasets are discussed, and future research directions are outlined in this study. Additionally, the paper highlights the practical outcomes achieved with economic LiDAR solutions, whose data densities are one to two orders of magnitude lower than those obtained with powerful and expensive solutions. Furthermore, the potential for integration with other technologies to enhance detection efficiency and precision is discussed.

中文翻译：

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利用光探测和测距技术进行路面检测的进展与创新

光探测与测距（LiDAR）是一种先进的非接触式感测方法，能够根据测距方法以毫米级精度捕捉三维空间数据，已被广泛应用于路面缺陷检测和道路资产管理。本文概述了基于激光雷达的路面检测技术，涵盖测量原理、采集方法表征以及点云数据的算法处理。随后，分析了主要激光雷达系统的特性，包括移动激光扫描（MLS）、地面激光扫描（TLS）和机载激光扫描（ALS），及其在路面信息检测中的适用性。MLS 因其卓越的移动性和测量精度，成为主要方法。随后，对传统和基于深度学习的三维点云处理算法进行了比较，讨论了在大数据集下实现高准确性和效率的挑战，并概述了未来的研究方向。此外，论文还强调了经济型激光雷达解决方案所取得的实际成果，其数据密度比强大且昂贵的解决方案低一个数量级到两个数量级。此外，还讨论了与其他技术整合以提升检测效率和精度的潜力。