Narrow Results

Positioning data with cm or mm level precision gives us a lot more possibilities than with low level (meter level) precision. Real-Time Kinematic (RTK) is a popular GNSS positioning method used in the case of high-precision applications. Only high-cost GNSS receivers provided better RTK solutions because the number of GNSS was not enough, but those were not affordable for many surveyors. The increase in the number of GNSS made low-cost GNSS receivers provide better RTK solutions recently, and ubiquitous networking connected to the internet enables rover-receivers to get parameters for correcting positioning signals of GNSS from station-receivers. In this study, a low cost and easy local-area RTK-GNSS system that uses existing GNSS satellites, the internet, 4G connection, and a caster server to provide real-time horizontal and vertical positioning within a few centimeters using NEO-M8P-2 module receiver with Raspberry Pi 3b+ as a station system and NEO-M8P-0 module receivers with a smartphone as a high mobility rover system, was developed and evaluated. The accuracy of the positionings (coordinates and height) was verified at each level point basically once. And to evaluate the effects of changes in environmental conditions, such as the positions of GNSS satellites, weather conditions, and 4G connection conditions, we have also conducted a series of precision surveying observations using local-area RTK-GNSS at the fixed point on the university campus over several months. It was found that this system was completely maintenance-free at least for this period and could always provide data with sufficient accuracy.

We constructed a low-cost and easy-to-handle local-area RTK-GNSS positioning system which can automatically perform RTK positioning every second. The positioning was conducted at two reference stations to evaluate the accuracy and reliability of the real-time data: (1) at the university campus (ground fixed point) with obstacles such as buildings and trees, hindering a GNSS satellite signal reception and (2) at the open-sky rooftop of a 11-story school building (rooftop fixed point), where the reference station had been set up with only few obstacles to GNSS satellite signal reception. At the ground station, the network connection status, temperature, and relative humidity were also simultaneously measured. Consequently, 600 data were acquired in 10 min, and the data with the highest ratio were further selected for analysis. For the ground-based fixed points, the standard errors of all the data were >10 times more accurate than those of D-GNSS; however, such result is unsatisfactory, as previously anticipated for RTK positioning. We further filtered the data with less than 15 satellites and less than 9 ratio values and found that the data quality and reliability were improved to a satisfactory level. For the rooftop fixed point, the standard error of all the data was approximately 1 mm in both horizontal and vertical directions, thereby indicating higher accuracy than the data from the ground fixed point. Consequently, we obtained accurate and reliable data with less scatter. Overall, it is possible to obtain accurate and reliable RTK positioning data in real time using statistical processing to eliminate outliers.