The performance of a low-cost, self-contained, compact, and easy to deploy mapping-grade mobile laser scanning (MLS) system, which is composed of a light detection and ranging sensor Velodyne VLP-16 and a dual antenna global navigation satellite system/inertial navigation system SBG Systems Ellipse-D, is analyzed. The field tests were carried out in car-mounted and backpack modes for surveying road engineering structures (such as roads, parking lots, underpasses, and tunnels) and coastal erosion zones, respectively. The impact of applied calculation principles on trajectory postprocessing, direct georeferencing, and the theoretical accuracy of the system is analyzed. A calibration method, based on Bound Optimization BY Quadratic Approximation, for finding the boresight angles of an MLS system is proposed. The resulting MLS point clouds are compared with high-accuracy static terrestrial laser scanning data and survey-grade MLS data from a commercially manufactured MLS system. The vertical, horizontal, and relative accuracy are assessed—the root-mean-square error (RMSE) values were determined to be 8, 15, and 3 cm, respectively. Thus, the achieved mapping-grade accuracy demonstrates that this relatively compact and inexpensive self-assembled MLS can be successfully used for surveying the geometry and deformations of terrain, buildings, road, and other engineering structures.
Numerous filtering algorithms have been developed in order to distinguish the ground surface from nonground points acquired by airborne laser scanning. These algorithms automatically attempt to determine the ground points using various features such as predefined parameters and statistical analysis. Their efficiency also depends on landscape characteristics. The aim of this contribution is to test the performance of six common filtering algorithms embedded in three freeware programs. The algorithms’ adaptive TIN, elevation threshold with expand window, maximum local slope, progressive morphology, multiscale curvature, and linear prediction were tested on four relatively large (4 to 8 km2) and diverse landscape areas, which included steep sloped hills, urban areas, ridge-like eskers, and a river valley. The results show that in diverse test areas each algorithm yields various commission and omission errors. It appears that adaptive TIN is suitable in urban areas while the multiscale curvature algorithm is best suited in wooded areas. The multiscale curvature algorithm yielded the overall best results with average root-mean-square error values of 0.35 m.