3D Mapping Database Aided GNSS Based Collaborative Positioning Using Factor Graph Optimization

Abstract

The recent development in vehicle-to-everything (V2X) communication opens a new opportunity to improve the positioning performance of the road users. We explore the benefit of connecting the raw data of the global navigation satellite system (GNSS) from the agents. In urban areas, GNSS positioning is highly degraded due to signal blockage and reflection. 3D building model can play a major role in mitigating the GNSS multipath and non-line-of-sight (NLOS) effects. To combine the benefits of 3D models and V2X, we propose a novel 3D mapping aided (3DMA) GNSS-based collaborative positioning method that makes use of the available surrounding GNSS receivers' measurements. By complementarily integrating the ray-tracing based 3DMA GNSS and the double difference technique, the random errors (such as multipath and NLOS) are mitigated while eliminating the systematic errors (such as atmospheric delay and satellite clock/orbit biases) between road user. To improve the accuracy and robustness of the collaborative algorithm, factor graph optimization (FGO) is employed to optimize the positioning solutions among agents. Multiple low-cost GNSS receivers are used to collect both static and dynamic data in Hong Kong and to evaluate the proposed algorithm by post-processing. We reduce the GNSS positioning error from over 30 meters to less than 10 meters for road users in a deep urban canyon.

GNSS RUMS: GNSS Realistic Urban Multi-agent Simulator for Collaborative Positioning Research

“…The proposed simulator is developed based on sophisticated models, covering most of the interferences in the urban area. As the results in Table 4, by applying different advanced positioning algorithms, the remaining positioning errors are consistent with the real experimental performances reported in [18,21,54]. Therefore, the proposed simulator can appropriately reflect the challenges of urban GNSS positioning for future studies, especially the potential algorithms hard to conduct experimental verification, such as the large-scale collaborative positioning algorithms.…”

“…(3) RT: 3DMA GNSS ray-tracing positioning for single agent [18]; (4) RT-CP: 3DMA GNSS ray-tracing based collaborative positioning with factor graph optimization [21]. Figure 23.…”

“…The predicted satellite visibilities of neighboring road agents from the 3D building model are collaborated to achieve better position estimation [19,20]. Moreover, a complementary integration of the 3DMA GNSS ray-tracing technique and the DD based GNSS CP is developed to eliminate the systematic error and mitigate the uncorrelated NLOS error simultaneously [21]. The integration of 3DMA GNSS and CP has excellent potential to achieve satisfactory positioning accuracy in the urban area.…”

“…Hence, it is inappropriate to validate the CP algorithm with the GNSS measurement error simulated by the normal distribution or other statistical distribution models. Some studies directly use real measurements collected from multiple receivers for validation [14,21], whereas the agent amount is limited to a few. It is tough to test large-scale performance with large numbers of agents or study the influence of network size.…”

Multi-Agent Collaborative GNSS/Camera/INS Integration Aided by Inter-Ranging for Vehicular Navigation in Urban Areas

“…However, the accuracy of relative measurements estimated in [28,30] suffers from the multipath effects and NLOS receptions in urban canyons. To solve this problem, our previous work in [31,32] goes one step further by making use of the 3D building model to mitigate the impacts of multipath and NLOS signals before estimating the inter-agent measurements. Besides, a novel weighting scheme is proposed to model the uncertainty of the measurements from different agents.…”