Maximum-Likelihood Aerial-to-Ground Landmark Matching for Drift Suppression in Lunar Visual–Inertial Navigation

Landmark matching from an aerial database to ground images (AtoG) remains underexplored. This paper proposes an enhancement to a visual–inertial system using AtoG landmark matching against a prior orbital database. The proposed algorithm employs a maximum-likelihood landmark association framework. The proposed approach integrates absolute position corrections derived from AtoG matching into two state estimation architectures, an extended Kalman filter and a pose-graph optimization framework. The method combines stereo-range measurements with a pretrained object detection network to identify and associate craters and rocks, avoiding sequential frame-to-frame feature tracking. Measurement uncertainty, including stereo-range variance and correlated orbital map errors, is incorporated into the likelihood computation. The algorithm is evaluated on a synthetic lunar traverse under low-solar-elevation conditions representative of high-latitude environments. Compared with standalone visual–inertial simultaneous localization and mapping as well as visual–inertial navigation system solutions, the accumulated drift is significantly reduced by fusing prior orbital information with onboard visual–inertial sensing.