GIVL-SLAM: A Robust and High-Precision SLAM System by Tightly Coupled GNSS RTK, Inertial, Vision, and LiDAR

In this article, we present GIVL-SLAM, a factor graph optimization-based framework that tightly fuses double-differenced pseudorange and carrier phase observations of the GNSS with inertial, visual, and LiDAR information for high-level simultaneous localization and mapping (SLAM) performance in large-scale environments. A sliding-window-based factor graph estimator is designed to explore the potential of heterogeneous observations from multiple sensors for achieving robust and high-accuracy state estimation. The integer ambiguity resolution of GNSS carrier phase observation is also considered in our method to leverage the high-precision characteristic of the carrier phase. We extensively evaluated the proposed method in real-world experiments including both GNSS-challenged environment tests and urban night environment tests. The results demonstrate that the proposed GIVL-SLAM significantly improves the global drift-free ability of the visual-inertial-LiDAR system in large-scale conditions and achieves continuous centimeter to decimeter-level localization performance in GNSS harsh-signal conditions. The maximum improvement of the 3-D location availability (<1 m) in GNSS severely degraded situations is more than 60% compared with the existing loosely coupled GNSS/SLAM fusion methods.