Adaptive V-I Fusion SLAM: Multi-Constraint Optimization for Illumination Robust Localization

This paper proposes an adaptive visible-infrared (V-I) multi-constraint fused SLAM system for illumination robust localization. It organizes the two modalities into a primary/ auxiliary scheme: the primary stream is selected by feature-tracking stability with a hysteresis mechanism, while auxiliary features are continuously weighted according to their incremental spatial complementarity measured by the surprisal of the primary feature-density distribution. The resulting weighted visual factors are jointly optimized with IMU preintegration and vehicle-motion constraints (forward velocity and kinematic constraints with zero-velocity updates when applicable) in a tightly-coupled sliding-window framework to reduce drift under degraded vision. We further adopt an illumination-adaptive hierarchical relocalization pipeline to improve place recognition across day-night transitions. Experiments on public and custom datasets demonstrate consistently improved robustness and accuracy over single-modality and direct fusion baselines under both stable and varying illumination.