Gravity-aware partially calibrated absolute pose estimation from affine- or rotation-covariant features

Inertial measurement units (IMUs) are now standard in mostconsumer devices, such as smartphones, drones, and extended reality(XR) headsets. By fusing visual and inertial data, localization systemsgain significantly in speed and robustness compared to vision-only orIMU-only approaches. However, traditional pose estimation methods failto utilize the local geometric information embedded in feature descriptorslike SIFT. Recent work has proved the advantages of leveraging this in-formation for relative and absolute pose estimation, but its application topartially calibrated absolute pose estimation remains unexplored. In thispaper, we derive novel constraints for joint estimation of absolute poseand focal length, making use of a gravity vector obtained from IMU dataand the feature-induced local geometry, which we use to construct twoefficient solvers: UP1PfAC, that operates given a single affine correspon-dence and UP2PfORI, which requires two orientation-covariant features.Unlike traditional, semi-calibrated absolute pose methods requiring fourpoint correspondences, our solvers benefit from fewer samples and lowercomputational cost, simplifying robust estimation in modern RANSAC-like frameworks. We evaluate the proposed solvers against the state-of-the-art on large-scale public datasets and demonstrate that our methodachieves fast and accurate localization and focal length estimation.