Artificial Intelligence-Enhanced Visual–Inertial Navigation for Autonomous Lunar Surface Mobility: Systems, Datasets, Methods, and Open Challenges

Reliable onboard state estimation plays an important role in autonomous lunar surface mobility, given the lack of conventional global navigation satellite system (GNSS) coverage and the challenges of variable illumination and terrain texture. This review synthesizes model-based, learning-based, and hybrid visual–inertial odometry (VIO) approaches and assesses their relevance to lunar surface operations. The reviewed studies suggest that learned components can improve feature extraction, matching, depth estimation, and measurement confidence in selected datasets and operating conditions. However, representative lunar validation and uncertainty calibration remain limited. Given these limitations, hybrid architectures integrating learned components with explicit model-based estimators offer a comparatively testable design direction. Reduced lunar gravity alone does not render monocular scale unobservable; practical scale–bias performance also depends jointly on motion excitation, inertial quality, visual geometry, calibration, and estimation-window length. Stereo, wheel, crater, and map cues can provide additional metric information but introduce source-specific risks involving calibration, slip, association, and registration. Within the resources surveyed, we found no publicly available flight-derived lunar traverse dataset combining synchronized camera–IMU measurements, independent trajectory ground truth, and onboard timing. Evidence-based priorities include representative lunar validation, uncertainty-aware integration of learned components, consistency monitoring across metric sources, explicit estimator health and fallback mechanisms, and target-hardware resource evaluation.