Tightly Coupled RTK-Visual-Inertial Integration With a Novel Sliding Ambiguity Window Optimization Framework

Accurate and reliable navigation is fundamental for intelligent transportation applications such as autonomous driving. Current research community has gained advances in global navigation satellite system (GNSS) and its fusion with visual-inertial navigation system (VINS). However, existing optimization-based integrations fail to fully utilize the constant characteristic of carrier phase ambiguity when facing frequent cycle slips and degraded GNSS signals, leading to erratic navigation output in complex environments. To address these limitations, this work proposes a tightly coupled GNSS real-time kinematic (RTK)-visual-inertial integration with a novel sliding ambiguity window optimization framework to achieve high-precision and robust positioning. Specifically, a sliding ambiguity window framework is built to associate float single-differenced ambiguities estimated by factor graph optimization and integer double-differenced ambiguities obtained by LAMBDA ambiguity resolution (AR). The framework can maintain continuous AR constraints across epochs and extend ambiguity-fixed solutions even during AR failures. Additionally, a VINS-aided single&dual-frequency hybrid cycle slip detection method is proposed, combining available dual-frequency GNSS observations and VINS information to perform reliable cycle slip detection for all single- and dual-frequency carrier phases. The superiority of the proposed method is verified by real-world vehicle traveling experiments in campus and urban scenarios. Results show that our proposed method can achieve robust centimeter-level positioning accuracy in both trajectories, outperforming the state-of-the-art optimization-based integration method by 72.2% and 66.0%, respectively.