A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems

Abstract

The network real-time kinematic (RTK) technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI), and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs), robotic equipment, etc.) require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC) approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC) according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS) data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

Developing Inspection Methodology of Solar Energy Plants by Thermal Infrared Sensor on Board Unmanned Aerial Vehicles

“…The flight path was determined by considering the degree of redundancy between the flight paths to fully cover the target area. The UAV image was determined by setting the image height, taking into consideration the focal length and pixel size of the sensor, and then determining the spatial In order to secure the position of the solar panel accurately, an RTK-VRS survey was performed based on the GRS80 TM coordinate system by selecting four GCPs [29,30]. Geometric correction was performed using GPS values in the UAV, and the data were converted into orthographic images using tie points automatically generated in the point cloud densification process.…”

“…The altitude for accurate matching of the two images was investigated by incrementally changing the spatial resolution of the two sensors by 10 m. The spatial resolution of a sensor is defined by a measure called ground sample distance (GSD) or pixel size. GSD in an orthographic image of the ground from space is the distance between pixel centers measured on the ground [30].…”

“…The results are shown in Figure 4, which shows the spatial resolution of each altitude of the thermal infrared image. In order to secure the position of the solar panel accurately, an RTK-VRS survey was performed based on the GRS80 TM coordinate system by selecting four GCPs [29,30]. Geometric correction was performed using GPS values in the UAV, and the data were converted into orthographic images using tie points automatically generated in the point cloud densification process.…”

“…After shooting, it can be output as digital data after it is merged with GCP data or the like, after image processing (creation of original image) and stereoization. Figure 5 shows the orthographic images of RGB and thermal infrared completed by linking with the GCP point using the RTK-VRS surveying technique for UAV images [30]. When shooting with UAV, the most sensitive element is to ensure the location accuracy of the problematic point.…”

Improving GNSS Ambiguity Acceptance Test Performance with the Generalized Difference Test Approach

“… can be calculated with: where is the determinant of the matrix; is the exponential operator. is the squared Mahalanobis distance operator [ 5 , 6 ], which can be calculated by .…”