3D Mapping Aided GNSS-Based Cooperative Positioning Using Factor Graph Optimization

The global navigation satellite system (GNSS) positioning accuracy is highly degraded due to signal reflections from buildings. A stand-alone receiver only has limited measurement capabilities, thus resulting in limited performance. With the rapid development of communications techniques, sharing measurements between receivers have become possible. Therefore, this study proposed a novel 3D mapping aided (3DMA) GNSS-based cooperative positioning method that makes use of all the available surrounding receivers’ measurements for acquiring better positioning solutions. By complementarily integrating the GNSS ray-tracing algorithm and the double difference technique, the uncorrelated errors are mitigated while eliminating the correlated errors between users. As a result, a more accurate relative positioning solution is achieved, which can further improve the absolute positioning accuracy of the degraded receiver. To improve the robustness of the cooperative positioning algorithm, factor graph optimization is employed to obtain an overall optimal positioning solution among multiple receivers’ solutions. By further integrating the 3DMA GNSS cooperative positioning with factor graph optimization, the positioning accuracy and robustness are improved, and the method achieves a root mean square error of less than 10 meters for most receivers in a dense urban area.