GIVE: A Tightly Coupled RTK-Inertial–Visual State Estimator for Robust and Precise Positioning

Continuous and reliable high-precision positioning in a wide area is an important task for autonomous driving. In this article, we present GIVE, a nonlinear optimization-based global navigation satellite system (GNSS)-inertial–visual (GIV) state estimator that tightly fuses multi-GNSS raw carrier phase and pseudorange observations with inertial and visual information to address the problem of continuous and reliable centimeter-level positioning in urban canyons where GNSS signal may be intermittent or even outage. The primary contribution of this work is the investigation of the high-precision carrier phase observations in terms of continuity and ambiguity resolution (AR) in GIV fusion. In particular, first, we propose a carrier phase-enhanced bundle adjustment (CPBA) method for accurate and drift-free GIV state estimation, in which the continuously tracked carrier phase arcs are used to establish the ambiguity constraints of current and historical states which have common-view satellites. The common-view ambiguity constraints are jointly optimized with IMU preintegration constraints and visual reprojection constraints under the factor graph framework. Furthermore, we develop a marginalization-based carrier phase ambiguity propagation (AP) method to leverage the continuity of the carrier phase and achieve an optimal estimation of the float ambiguity, which is essential for the correct AR of the carrier phase. An integer AR module, in combination with the proposed CPBA, enables the centimeter-level positioning accuracy of the GIV framework. We extensively evaluate the proposed method in both public datasets and challenging datasets collected in deep urban environments. Statistics show that the GIVE can provide smooth centimeter-level positioning results in open-sky conditions. Two challenging urban driving tests demonstrate that the GIVE can achieve continuous centimeter- to decimeter-level positioning accuracy and obtain 86.5%–94.1% 2-D positioning availability (
${< }10$
cm) and more than 94% 3-D positioning availability (
${< }30$
cm) in deep urban environments.