Automated Rutting Measurement Using an Inexpensive RGB-D Sensor Fusion Approach

Abstract

Pavement management systems have become more crucial through the development of road networks. Data collection is at the core of any pavement management system. Rutting is a critical defect on pavements that can lead to hydroplaning and accidents. In this study, a system is proposed to quantify the rut depths autonomously by utilizing an inexpensive red green blue—depth (RGB-D) sensor. To this end, a cart is designed, built, and used to mount an array of RGB-D sensors for data collection purposes. Furthermore, a procedure is proposed for autonomous stitching of three-dimensional (3D) point clouds obtained from multiple RGB-D sensors to generate the pavement profile of a lane. Next, transverse profiles are extracted and the rut depths are estimated based on the simulated straight-edge method. Several field experiments are conducted to evaluate the capabilities as well as the limitations of the proposed system. Results show that the proposed approach is capable of estimating different severity levels of rutting accurately, particularly moderate and high severity levels with relative errors of 15% and 9%, respectively. Additionally, the repeatability of the proposed approach is evaluated and confirmed.