Precise, seamless, and high-rate navigation remains a major challenge, particularly when relying on low-cost sensors. With the decreasing cost of cameras, Inertial Measurement Units (IMUs), and Global Navigation Satellite System (GNSS) receivers, tightly coupled fusion frameworks, such as GVINS, have gained considerable attention. GVINS is an optimization-based factor-graph framework that integrates visual and inertial measurements with single-frequency GNSS-code pseudorange observations to provide robust and drift-free navigation. This study aimed to evaluate the potential of applying GVINS to low-cost, low-power, and single-frequency GNSS receivers, particularly those embedded in smartphones, by integrating 1 Hz GNSS measurements collected in three challenging urban scenarios into the GVINS framework to produce seamless 10 Hz positioning estimates. The experiments were conducted using an Xsens MTi-1 IMU and global-shutter (GS) cameras, as well as a Samsung A51 smartphone and a u-blox ZED-F9P GNSS receiver. GVINS was modified to process 1 Hz GNSS measurements. Differential corrections from a nearby GNSS reference station were also incorporated to assess their impact on optimization-based filters, such as GVINS. The performance of GVINS and Differential GVINS (D-GVINS) solutions using smartphone measurements was compared against standard point positioning (SPP) and differential GPS (DGPS) results obtained from the same smartphone GNSS receiver, as well as the GVINS solution derived from u-blox ZED-F9P measurements sampled at 1 Hz. Experimental results show that GVINS effectively operates with smartphone GNSS measurements, reducing 3D RMS errors by 80.4%, 64.9%, and 83.8% for the sports field, campus-walking, and campus-driving datasets, respectively, when differential corrections are applied relative to the SPP solution. These results highlight the potential of smartphone GNSS receivers within the GVINS framework: Even though they observe fewer constellations, lower signal quality, and a lower number of satellites, they can still achieve a performance comparable to that of a relatively higher-end dual-frequency GNSS receiver, the u-blox ZED-F9P. Further studies will focus on adapting the GVINS algorithm to run directly on smartphones to utilize all the available measurements, including the camera, IMU, barometer, magnetometer, and additional ranging sensors.

This study aims to investigate the comparative performance of PPP technique in evaluating the precision of three distinct geoid models and to quantify the contribution of GLONASS constellation to GPS as well as the effect of observation length. We use recently and concurrently measured GNSS and levelling data crossing rough topography with steep geoid slope in Türkiye. The RMSE statistics about the mean of the geometric minus gravimetric geoid heights reveal that at least 4-hours of static data should be collected regardless of the constellation. We find out encouraging results with 8-hours of GPS + GLONASS solutions having RSME values of 3.0, 5.0 and 21.5 cm in validating national, globally combined and satellite-only geoid models, respectively. The multi-GNSS PPP processing with a relatively long observation period yields comparable results to relative positioning though the latter still performs slightly better.

Terrestrial mobile gravimetry using temperature-stabilized inertial measurement units (IMUs) is emerging as an efficient technique for acquiring high-accuracy gravity data in dynamic environments. This study explores the integration of the iCORUS-2 IMU system with GNSS along the Cebeci-Elmadağ route in Ankara, Türkiye. The iTempStab technology was incorporated into the iCORUS-2 system to stabilize temperature variations to enhance the accuracy of gravity measurements. Notably, this research represents the results of vehicle-based terrestrial mobile gravimetry using a thermally stabilized IMU. An extended Kalman filter (EKF) was applied to integrate IMU and GNSS data. The advantage of terrestrial mobile gravimetry over airborne methods is the ability to make static updates for vertical gravity, zero gravity gradient and zero velocity measurements, which can be introduced into the EKF as measurement updates. The method was tested by analysing the statistical impact of these updates at varying intervals: without any gravity measurement updates, and by applying updates at approximately 2 km, 3 km, 4 km, 5 km, 6 km, and 7 km intervals. Results indicated that without updates, a root mean square error (rmse) value between external terrestrial gravity data and mobile gravimetry estimates is 1.362 mGal. However, with updates at approximately 2 km intervals, the rmse value significantly decreased to 0.948 mGal, representing a %30.4 reduction in rmse and achieving sub-mGal accuracy. The inclusion of temperature stabilization and frequent gravity measurement updates demonstrated a marked improvement in data accuracy and consistency. These advancements not only validate the potential of mobile gravimetry for geophysical surveys but also broaden its applications in fields requiring precise gravity data acquisition.