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Visual localization allows autonomous robots to relocalize when losing trackof their pose by matching their current observation with past ones. However,ambiguous scenes pose a challenge for such systems, as repetitive structurescan be viewed from many distinct, equally likely camera poses, which means itis not sufficient to produce a single best pose hypothesis. In this work, wepropose a probabilistic framework that for a given image predicts thearbitrarily shaped posterior distribution of its camera pose. We do this via anovel formulation of camera pose regression using variational inference, whichallows sampling from the predicted distribution. Our method outperformsexisting methods on localization in ambiguous scenes. Code and data will bereleased at https://github.com/efreidun/vapor.

A Probabilistic Framework for Visual Localization in Ambiguous Scenes