Semi-tightly coupled integration of multi-GNSS PPP and S-VINS for precise positioning in GNSS-challenged environments

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.

GVINS: Tightly Coupled GNSS–Visual–Inertial Fusion for Smooth and Consistent State Estimation

“…The methods in [10] and [11] and our previously proposed VINS-Fusion [12] fuse the results from local visual-inertial odometry (VIO) with GNSS solutions under the optimization framework. In [13], the authors combine the results from precise point positioning (PPP) [14] with stereo VIO to achieve low-drift estimation. Both the GNSS code and phase measurements are used in their formulation and precise satellite products are utilized to improve the accuracy.…”

From SLAM to Situational Awareness: Challenges and Survey

“…Qin et al in [99] present a generic factor graph based framework for fusing several several sensors, where each sensor serves as a factor connected with the state of robot, easily adding them to the optimization problem. Li et al [100] propose a graph based sensor fusion framework for fusing Stereo Visual Inertial Navigation System (S-VINS) with mutli-GNSS data in a semi-tightly coupled manner, where the S-VINS output is fed as an initial input to the position estimate from the GNSS system in challenging GNSS deprived environments, thus improving the overall global pose estimate of the robot.…”

A Method of Vision Aided GNSS Positioning Using Semantic Information in Complex Urban Environment

“…Though all of these works report satisfactory results, the optimization-based approach is lack of discussing the potential for GPS failures, and the filter-based approach did not take advantage of the semantic information of images. In [28][29][30], the integration of a camera, INS and GNSS allows the GNSS to maintain positioning accuracy in a GNSS-challenged environment. In [31], the authors incorporated carrier phase differential GPS measurements into the bundle-adjustment based visual SLAM framework to obtain high-precision globally referenced positions and velocities.…”