Photogrammetric Pavement Detection System

This paper introduces a complex, low-cost road pavement measurement system designed primarily for pothole and crack detection. The onboard system is composed of a GPS/INS navigation unit, an image acquisition module, and a photogrammetric and imageprocessing subsystem. Due to the use of structured light and the availability of accurate navigation data, the 3D coordinates of the road points, in the form of cross profiles, as well as the size and location of the potholes, can be derived from the images at cm-level relative accuracy. For the road classification the International Roughness Index (IRI) can be derived. This paper also discusses the potential future developments of the system; it reports on the initial investigations of applying single camera with laser line projection which can significantly simplify the overall procedure. * Corresponding author. 1. INRODUCTION AND CONCEPT In the well-motorized countries the transportation authorities spend significant amounts of money to road constructions and road pavement maintenance. The roads are aging; the maintenance works have to keep pace with the new constructions. The traffic delays are mostly caused by road closures, this topic is always highlighted in the news. Therefore, the optimized scheduling became extremely important, the authorities tend to turn to new technologies in order to map the actual condition of the road pavement and decide upon the priorities of the scheduled maintenance works. Since road engineering is one of the most extended engineering field, many “traditional” road surveying methods used for construction and maintenance planning. As most of the maintenance works are scheduled based on experiences, the road conditions are often classified by visual observations. The number of potholes, the length and type of the cracks, and lane grooves are clear indicators of the overall road condition. However, there are a few autonomous (or at least semiautonomous) systems in use, e.g. the Swedish RST (http://www.opq.se) or the Mandli Roadview (http://www.mandli.com): both systems are able to measure the crossand longitudinal profiles of the roads; moreover, Mandli is collecting digital images about the road surface. This paper discusses the structure and results of the original system, the potential of the entry-level IMUs (inertial measurement unit) in mobile mapping and introduces the latest concept with the initial test results. 2. SYSTEM COMPONENTS Our basic idea was to create a road pavement detection system, which uses optical sensors for measuring the longitudinal and cross sections of the roads and the pavement anomalies (potholes, wide cracks). Figure 1 shows how the system components are linked to each other. The sensor component consists of two cameras (Sony XCD-SX910) synchronized by external trigger and laser projectors, the location component contains an integrated GPS/INS (Global Positioning System / Inertial Navigation System) unit (Crossbow NAV420CA). The cameras are capturing downward images about the pavement at a rate of 5 fps. 20 individual laser projectors provide the equally spaced marker points located perpendicular to the travelling direction on the surface. Therefore, the 3D coordinates of the points can be computed by evaluating equations of geometric constraints. The georeferenced position and orientation of the camera pair are provided by the integrated GPS/INS system. The NAV420CA system consists of a navigation grade GPS receiver, an entry-level IMU (Inertial Measurement Unit), and a built-in Kalman-filter. The accuracy of the positioning by the GPS module is about 3 m CEP, whereas the random walk of the IMU is about 4.5°/hr1/2 (Barsi et al. 2005). The cameras have 1280 by 960 pixel resolution which results in 3 mm ground pixel size, which enable not only the detailed surface description but the scar and crack detection from the images, too. The camera location assures the full lane-width visibility, covering 3.5 m wide area. The cameras are linked by FireWire to the laptop mounted in the cab (Figure 2).