Abstract

Because of its high-precision, low-cost and easy-operation, Precise Point Positioning (PPP) becomes a potential and attractive positioning technique that can be applied to self-driving cars and drones. However, the reliability and availability of PPP will be significantly degraded in the extremely difficult conditions where Global Navigation Satellite System (GNSS) signals are blocked frequently. Inertial Navigation System (INS) has been integrated with GNSS to ameliorate such situations in the last decades. Recently, the Visual-Inertial Navigation Systems (VINS) with favorable complementary characteristics is demonstrated to realize a more stable and accurate local position estimation than the INS-only. Nevertheless, the system still must rely on the global positions to eliminate the accumulated errors. In this contribution, we present a semi-tight coupling framework of multi-GNSS PPP and Stereo VINS (S-VINS), which achieves the bidirectional location transfer and sharing in two separate navigation systems. In our approach, the local positions, produced by S-VINS are integrated with multi-GNSS PPP through a graph-optimization based method. Furthermore, the accurate forecast positions with S-VINS are fed back to assist PPP in GNSS-challenged environments. The statistical analysis of a GNSS outage simulation test shows that the S-VINS mode can effectively suppress the degradation of positioning accuracy compared with the INS-only mode. We also carried out a vehicle-borne experiment collecting multi-sensor data in a GNSS-challenged environment. For the complex driving environment, the PPP positioning capability is significantly improved with the aiding of S-VINS. The 3D positioning accuracy is improved by 49.0% for Global Positioning System (GPS), 40.3% for GPS + GLOANSS (Global Navigation Satellite System), 45.6% for GPS + BDS (BeiDou navigation satellite System), and 51.2% for GPS + GLONASS + BDS. On this basis, the solution with the semi-tight coupling scheme of multi-GNSS PPP/S-VINS achieves the improvements of 41.8–60.6% in 3D positioning accuracy compared with the multi-GNSS PPP/INS solutions.

Highlights

  • Precise Point Positioning (PPP) has been demonstrated as an effective tool in high-precision positioning and shows the advantages of efficiency and flexibility compared to the baseline network approach (Zumberge et al 1997; Bisnath and Gao 2009)

  • To improve the positioning performance in GNSSchallenged environments, an optimization-based semi-tightly coupled multi-sensor fusion framework of multi-Global Navigation Satellite System (GNSS) PPP/S-Visual-Inertial Navigation Systems (VINS) was developed and validated in this study

  • Based on the GNSS outage simulation test and the vehicle-borne experiment, the positioning performances of the multi-GNSS PPP/Stereo VINS (S-VINS) solution were comprehensively evaluated with respect to the stand-alone S-VINS positioning, the S-VINS aided multi-GNSS PPP positioning, and the triple integrated system positioning

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Summary

Introduction

Precise Point Positioning (PPP) has been demonstrated as an effective tool in high-precision positioning and shows the advantages of efficiency and flexibility compared to the baseline network approach (Zumberge et al 1997; Bisnath and Gao 2009). System (BDS) and European Galileo navigation satellite system (Galileo) brings new opportunities for PPP. A four-system PPP model was proposed by Li et al (2015) to fully use the Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), Galileo, and BDS observations. The investigation of multi-GNSS PPP data processing is about the dual-frequency models (Cai et al 2015), Li et al Satell Navig (2021) 2:1 and focusing on the multi-frequency observations (Li et al 2019b, 2020a, b). The multi-frequency and multi-GNSS based PPP is becoming increasingly fashionable for precise positioning services (Alkan and Öcalan 2013; Guo et al 2018), in some new applications such as self-driving cars and unmanned aerial vehicles (Nie et al 2019; Geng and Guo 2020)

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