Pothole Mapping and Patching Quantity Estimates using LiDAR-Based Mobile Mapping Systems

Abstract

Pavement distress or pothole mapping is important to public agencies responsible for maintaining roadways. The efficient capture of 3D point cloud data using mapping systems equipped with LiDAR eliminates the time-consuming and labor-intensive manual classification and quantity estimates. This paper proposes a methodology to map potholes along the road surface using ultra-high accuracy LiDAR units onboard a wheel-based mobile mapping system. LiDAR point clouds are processed to detect and report the location and severity of potholes by identifying the below-road 3D points pertaining to potholes, along with their depths. The surface area and volume of each detected pothole is also estimated along with the volume of its minimum bounding box to serve as an aide to choose the ideal method of repair as well as to estimate the cost of repair. The proposed approach was tested on a 10 mi-long segment on a U.S. Highway and it is observed to accurately detect potholes with varying severity and different causes. A sample of potholes detected in a 1 mi segment has been reported in the experimental results of this paper. The point clouds generated using the system are observed to have a single-track relative accuracy of less than ±1 cm and a multi-track relative accuracy of ±1–2 cm, which has been verified through comparing point clouds captured by different sensors from different tracks.

Pavement Distress and Debris Detection using a Mobile Mapping System with 2D Profiler LiDAR

“…The same datasets were also used to compare the current strategy with the one proposed by Ravi et al (18), and the detections and reported severity of the potholes were found to be similar from the two strategies, thus validating the current method. As mentioned earlier, whereas the current strategy is designed to detect potholes directly from 3D point cloud, the one developed by Ravi et al (18) relies on additional information about the vehicle trajectory during data acquisition to achieve accurate results.…”

“…This implies that the approach in Kang and Choi ( 17 ) cannot be applied to MMSs moving at a considerably high speeds, which are required to map large roadway networks. Ravi et al proposed an approach for pothole detection using 3D point clouds captured by mobile LiDAR mapping systems moving at 40 to 50 mph ( 18 ). Their approach was based on 2D gridding of 3D point cloud along the road followed by an iterative 3D plane fitting within each grid to identify points that are deviating from the resultant best-fitting plane.…”

“…For the MMS used in this study, the relative accuracy of the system, that is, the alignment between the point clouds obtained from the different sensors in a given track, is less than 1 cm ( 18 ). One should note that the accuracy of anomaly detection will be largely dependent on the relative accuracy of the 3D point cloud, especially for anomalies such as mild cracking or small-sized FOD with a deviation as low as 2 to 3 cm from the expected pavement surface.…”

Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization

“…Data collected by MLMS has been used for extracting a wide range of road features such as pavement surfaces, lane markings, road edges, traffic signs, and roadside objects. It also facilitated applications including cross-section extraction [27,28], pavement condition monitoring [17], sight distance assessment [20,21], vertical clearance evaluation [22,29], and flood modeling in urban areas [7,8]. When compared with airborne LiDAR, ground systems provide a higher horizontal accuracy owing to their smaller laser footprint size.…”

“…Mobile LiDAR mapping systems (MLMS) have emerged as a prominent tool for collecting high-quality, dense point clouds in an efficient manner. Previous studies reported on the use of MLMS for automated lane marking detection [13,14], road centerline extraction [15], runway grade evaluation [16], debris/pavement distress inspection [17], traffic sign extraction [18,19], and sight distance assessment [20,21]. Mapping ditches using high-resolution LiDAR can be an efficient alternative to fielding surveys for prioritizing and planning ditch maintenance.…”