Journal of Surveying Engineering

Precise point positioning-real-time kinematic (PPP-RTK) positioning combines the advantages of PPP and RTK, which enables the integer ambiguity resolution (IAR) without requiring a reference station nearby. The ionospheric corrections are delivered to users to enable fast IAR. For large-scale networks, precise interpolation of ionospheric delays is challenging. The ionospheric delays are often independently estimated by the user, in the so-called ionosphere-float mode. The augmentation of low Earth orbit (LEO) satellites can bridge this shortcoming thanks to their fast speeds and the resulting rapid geometry change. Using 30-s real dual-frequency Global Positioning System (GPS) and Beidou Navigation Satellite System (BDS) observations within a large-scale network of thousands of kilometers, this contribution tests the effects of LEO augmentation using simulated dual-frequency LEO signals from the navigation-oriented LEO constellation, CentiSpace. Results showed that the LEO augmentation makes the solution convergence less sensitive to the original Global Navigation Satellite System (GNSS)-based model strength. The improvements in the convergence times are significant. For example, in the kinematic mode, the convergence time of the 90% lines of the GPS/BDS-combined ambiguity-float horizontal solutions to 0.05 m is shortened from more than 60 to 3.5 min, and that of the GPS-only partial ambiguity resolution (PAR)-enabled horizontal solutions is shortened from more than 20 to 4.5 min. In both the ambiguity-float and PAR-enabled cases, the 68.27% ($1\sigma$) lines of both the kinematic and static horizontal and height errors can converge to 0.05 m within 4 min, and for the 90% lines, within 6.5 min in all cases. The 90% line of the GPS/BDS/LEO combined PAR-enabled solutions can converge to 0.05 m within 2.5 and 3 min in the horizontal and up direction, respectively. Results also showed that enlarged projection of the mismodeled biases on the user coordinates were observed in the LEO-augmented scenario after convergence or ambiguity resolution. This is mainly due to the lower orbital height and low elevation angles of the LEO satellites, which requires further research when real LEO navigation signals are available.

The triple-frequency global positioning system (GPS), BeiDou navigation satellite system-3 (BDS-3), and Galileo can form the geometry-free and ionosphere-free (GFIF) carrier phase and pseudorange combinations, respectively. These combinations provide a precondition for which the accuracies of the carrier phase and pseudorange observations can be evaluated, respectively. The stochastic model of precise point positioning (PPP) processing is constructed using the evaluated results. The 176 International GNSS Service (IGS) stations are used to validate the presented method. The evaluated results show that accuracies of the GPS, BDS-3, and Galileo carrier phase observations are at the millimeter level, while those of pseudorange observations reach the decimeter level. In addition, BDS-3, GPS, and Galileo have different precisions. The stochastic model, which is reestablished from the evaluated accuracies, is tested using single Global Navigation Satellite System (GNSS) and multiGNSS PPP performances. The single-GNSS PPP shows that its convergence time has a mean improvement of 24%, and its 1- and 2-h mean three dimensions (3D) positioning accuracies are improved by about 17.7% and 4.5%, respectively, compared with those of empirical accuracies-based stochastic models. It also has the best performance in the multiGNSS PPP. Its mean convergence time of GPS+Galileo for 3 days is shorter than 35 min, while that of GPS+BDS-3 is less than 30 min. The mean improvements for the convergence time and 1- and 2-h 3D positioning reach 28%, 9.4%, and 2.8%, respectively, when the GPS+Galileo and GPS+BDS-3 PPP use the reestablished stochastic model instead of the empirical methods.

Inconsistent geoid heights may introduce uncertainties that limit its use in applications. Consequently, the scientific community has been collaborating to determine more rigorous geoid models in order to improve the accuracy of orthometric heights estimated with height transformation from the Global Navigation Satellite System (GNSS) positioning techniques. In this regard, this study evaluated the influence of different parameters (such as mass density, topographic models, geopotential models, and Stokes kernel modifications) used in the determination of a gravimetric geoid model through the remove-compute-restore (RCR) technique to contribute to these efforts. As a result of the analyses, it was determined that the Stokes kernel modification type and the global geopotential model selection significantly influence the improvement of geoid models in the study area (state of São Paulo, Brazil). However, the resolution of digital terrain models and the values of the lateral topographic density have less of an impact on the model’s accuracy when determining the geoid. In order to acquire findings that are consistent with geoid heights estimated by each determination technique, it is necessary to conduct a rigorous study of the employed data and computation parameters as a preliminary process.

Forest environment and topographic obstacles tend to reduce the positioning performance of precise point positioning (PPP) with ambiguity resolution (AR) and may even prevent radio signals from reaching the global navigation satellite systems (GNSS) antenna. In this study, we investigated the positioning performance of PPP-AR in a forest environment in terms of the crown closure ratios, session duration (1-, 2-, 3-, and 6-h), and different satellite constellations [i.e., the global positioning system (GPS)-only and $\mathrm{GPS}+\mathrm{GLONASS}$ combined satellites]. For this purpose, three GNSS receivers were used to make measurements at three test points in areas with crown closure ratios of 0%, 38%, and 87%. The data were evaluated using the PRIDE PPP-AR software and Canadian Spatial Reference System-PPP (CSRS-PPP). The experiments revealed that the inclusion of the GLONASS observations in the GPS-only solutions did not obviously improve the positioning error and accuracy with closure ratios of 0% and 38%. However, the improvements became more dramatic when the closure ratio increased to 87%. Furthermore, in the horizontal components, an accuracy of 10 cm can be achieved with at least a 2-h session, whereas for the up component, this level of accuracy can only be achieved with a 3-h session. While the PRIDE PPP-AR was able to achieve a 3D positioning performance of 1 cm with the combined $\mathrm{GPS}+\mathrm{GLONASS}$ satellites, this accuracy level remained at 8 cm in CSRS-PPP.

Road networks are essential elements of a community’s infrastructure and need regular inspection. Present practice requires traffic interruptions and safety risks for inspectors. The road detection system based on vehicle-mounted lasers is also quite mature, offering advantages such as high-precision defect detection, high automation, and fast detection speed. However, it does have drawbacks such as high equipment procurement and maintenance costs, limited flexibility, and insufficient coverage range. Therefore, this paper proposes a low-cost unmanned aerial vehicle (UAV)-based alternative using imagery for automatic road pavement inspection focusing on pothole detection and classification. A slicing-based method, entitled the Pavement Pothole Detection Algorithm, is applied to the imagery after it is converted into a three-dimensional point cloud. When compared with manually extracted results, the proposed UAV-structure-from-motion (SfM) method and the associated algorithm achieved 0.01 m level accuracy for pothole depth detection and maximum errors of $0.0053{\mathrm{m}}^3$ in volume evaluation for cases studies of both a road and a bridge deck.