Journal of Surveying Engineering

Many transportation agencies in the United States are transitioning to using the International Roughness Index (IRI) in pavement smoothness evaluation as part of an incentive/disincentive program for contractors paving roads. This study evaluates the use of digital levels, inertial profilers, inclinometer-based profilers, and terrestrial laser scanning (TLS) in pavement profile capture and analyses. Traditionally, analyses evaluated elevation differences between profiles. However, evolving standards and procedures perform incremental slope-based analyses to calculate IRI and correlate between profiles, which correspond better to vehicle response. The results of this study show consistent elevation profiles between all of the devices, when operated properly. However, digital levels cannot be used efficiently to capture the road profile at high sampling ($\text{<}10\hspace{0.17em}\text{cm}$) intervals for long profiles. Thus, TLS shows additional advantages compared with the other techniques, including the ability to collect a large, dense set of data relatively quickly for the entire roadway and surrounding areas; hence, the data can serve multiple purposes, including validation of other design parameters such as transverse and longitudinal slope. However, TLS requires substantially more processing and training.

Light detection and ranging (LIDAR)-based mapping systems are a well-established technology for the acquisition of three-dimensional (3D) point clouds. Although the results have generally been promising, uncompensated systematic errors in overlapping LIDAR strips cause horizontal and vertical shifts relative to each other. These LIDAR systematic errors are caused by the LIDAR system mounting parameters (boresight misalignment), mirror angles, and range biases. Global positional system/inertial measurement unit (GPS/IMU) drift in position and orientation can also contribute to these systematic errors if a low performance or improperly aligned GPS/IMU system is used. The proposed work demonstrates a 3D simultaneous multiswath block adjustment of overlapping LIDAR strips using elevation and intensity data to remove the LIDAR systematic errors without reference to the original navigational data. This can be achieved using ground control or LIDAR strips flown in different directions (parallel and orthogonal). The methodology presented takes a data-driven approach; therefore, it does not require access to the LIDAR raw data or navigational data, which in many cases is not available to the user. More generally, the methodology presented can be applied to other digital elevation models (DEMs), e.g., generated by photogrammetry or interferometric synthetic aperture radar (IFSAR).

Small unmanned/uninhabited aerial vehicles (UAVs) are quickly becoming a cost-effective alternative for mapping and volumetrics, particularly for small- to medium-sized earthwork projects. However, the accuracy of photogrammetrically derived digital terrain models (DTMs) from UAV imagery is not extensively tested. This gap is addressed through a case study of stockpile volume estimation. A gravel stockpile was surveyed with a vertical take-off and landing UAV before and after a portion of it was excavated. Softcopy photogrammetry was applied to the UAV images to produce before and after DTMs, each with a resolution of 3.5 cm. The vertical accuracy of the UAV DTMs was estimated with global positioning system (GPS) test points, yielding RMS errors of 0.106 (before) and 0.097 m (after). These errors are similar to, if not lower, than those produced by airborne light detection and ranging (LIDAR), in general, but are higher than for terrestrial laser scanning. The before and after volume of the stockpile estimated from the UAV DTMs was 2.6 and 3.9% lower than the volume estimated from GPS-interpolated DTMs. By differencing the two UAV DTMs, the volume of gravel extracted from the stockpile was estimated and compared with a haul ticket. According to the latter, the extracted volume was $\mathrm{1,483.440}\hspace{0.17em}{\text{m}}^3$, which was 2.5% lower than the volume derived from the difference between the UAV DTMs ($\mathrm{1,521.440}\hspace{0.17em}{\text{m}}^3$). Overall, results from this study indicate that UAV-based volumetrics can yield measurements that are similar to conventional techniques. In some cases, they may offer a more cost-effective, safer, and efficient technique for surveying at earthwork projects.