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.

中文翻译：

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多GNSS PPP和S-VINS的半紧密耦合集成，可在面临GNSS挑战的环境中进行精确定位

由于其高精度，低成本和易于操作，精确点定位（PPP）成为一种潜在且有吸引力的定位技术，可应用于自动驾驶汽车和无人机。但是，在全球导航卫星系统（GNSS）信号经常被阻塞的极端困难条件下，PPP的可靠性和可用性将大大降低。惯性导航系统（INS）已与GNSS集成在一起，以改善这种情况。近来，具有惯用互补特性的视觉惯性导航系统（VINS）被证明比仅使用INS能够实现更稳定和准确的局部位置估计。尽管如此，系统仍然必须依靠全局位置来消除累积的错误。在这项贡献中，我们提出了多GNSS PPP和立体声VINS（S-VINS）的半紧密耦合框架，该框架在两个单独的导航系统中实现了双向位置转移和共享。在我们的方法中，通过基于图优化的方法，将S-VINS产生的本地位置与多GNSS PPP集成在一起。此外，还可以反馈S-VINS的准确预测位置，以帮助GNSS挑战环境中的PPP。GNSS中断模拟测试的统计分析表明，与仅INS模式相比，S-VINS模式可以有效地抑制定位精度的降低。我们还进行了一项车载实验，在受到GNSS挑战的环境中收集多传感器数据。对于复杂的驾驶环境，借助S-VINS可以显着提高PPP定位能力。全球定位系统（GPS）的3D定位精度提高了49.0％，GPS + GLOANSS（全球导航卫星系统）的GPS提升了40.3％，GPS + BDS（北斗导航卫星系统）的GPS提升了45.6％，GPS + GLONASS提升了51.2％ + BDS。在此基础上，与多GNSS PPP / S-VINS的半紧耦合方案相比，该解决方案的3D定位精度提高了41.8–60.6％。