Cost-effective LiDAR for pothole detection and quantification using a low-point-density approach

Pothole-induced vehicle damage and accidents have significantly increased recently, motivating urgent needs for effective detection and maintenance strategies. This paper introduces an algorithm optimized for low-cost LiDAR sensors that improves the detection and quantification of potholes on road surfaces. The algorithm uses curvature-based analysis to detect potholes in spatially thinned, structured LiDAR datasets and assesses their size through boundary delineation and voxelization. Testing on high-resolution LiDAR scans in Edmonton, Alberta demonstrated consistent detection of varying pothole sizes and shapes, with measurements matching manual LiDAR analysis. Statistical sensitivity analysis revealed that reducing point density significantly to 205 points/m 2 (ppsm) had no measurable impact on detection and geometric assessment accuracy, maintaining measurement errors consistently within 3%–10%. The algorithm proved highly efficient with processing times of 88”/km and 23”/km for test segments with reduced point density, suggesting potential integration with city fleet vehicles for continuous and automated road maintenance monitoring. • Investigating the use of low-cost LiDAR sensors to detect pavement potholes. • Effective algorithms to detect potholes and gauge severity using sparse point clouds. • Investigating the trade-off between pothole characterization and LiDAR point density.