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- W4383172027 abstract "Visual localization allows autonomous robots to relocalize when losing track of their pose by matching their current observation with past ones. However, ambiguous scenes pose a challenge for such systems, as repetitive structures can be viewed from many distinct, equally likely camera poses, which means it is not sufficient to produce a single best pose hypothesis. In this work, we propose a probabilistic framework that for a given image predicts the arbitrarily shaped posterior distribution of its camera pose. We do this via a novel formulation of camera pose regression using variational inference, which allows sampling from the predicted distribution. Our method outperforms existing methods on localization in ambiguous scenes. We open-source our approach and share our recorded data sequence at github.com/efreidun/vapor." @default.
- W4383172027 created "2023-07-05" @default.
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- W4383172027 date "2023-05-29" @default.
- W4383172027 modified "2023-10-01" @default.
- W4383172027 title "A Probabilistic Framework for Visual Localization in Ambiguous Scenes" @default.
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- W4383172027 doi "https://doi.org/10.1109/icra48891.2023.10160466" @default.
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