AIO-NAV: INS-Centric All-In-One Navigation Framework Integrating Mono Camera, GNSS, and HD Map for Large-Scale Autonomous Driving

Current Visual-Inertial Odometry (VIO) systems degrade rapidly when visual input is compromised, and fail to fully exploit the observability of industrial-grade Inertial Measurement Units (IMUs). Unlike these visual-centric approaches, this paper presents AIO-NAV, an Inertial Navigation System (INS)-centric All-In-One Navigation framework that addresses these limitations. This framework treats INS as the backbone and integrates aiding sources including Global Navigation Satellite System (GNSS), monocular Visual Odometry (VO), and High-Definition (HD) map. All sensors are fused into a single navigator via a unified Extended Kalman Filter (EKF), which employs a modular design. Key update mechanisms are proposed to enable each aiding source to be independently activated or deactivated while maintaining stable operation. Moreover, each module can be easily maintained or replaced with improved solutions. A proposed invariant VO aiding module substitutes the traditional odometer by providing full 3D relative motion measurements instead of distance-only constraints. HD map-based point cloud registration provides advanced aiding, replacing loop-closure mechanisms for absolute pose corrections when the map is available. Comprehensive experiments in GNSS-challenging environments demonstrate that AIO-NAV significantly outperforms benchmarks, including both system-level (Xsens Vision Navigator) and algorithm-level (VINS-Fusion) comparisons. The system achieves a 3D root mean square error (RMSE) of 1.510 m compared to 5.151 m and 4.965 m, respectively. With HD map aiding, AIO-NAV-HDMAP achieves sub-meter level accuracy (0.795 m maximum error), making it suitable for high-level autonomous driving applications.