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Unsupervised Metric Relocalization Using Transform Consistency Loss

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Mike Kasper (University of Colorado)*; Fernando Nobre (Amazon); Christoffer Heckman (University of Colorado); Nima Keivan (Amazon)

Interactive Session

2020-11-18, 11:50 - 12:20 PST | PheedLoop Session


Training networks to perform metric relocalization traditionally requires accurate image correspondences. In practice, these are obtained by restricting domain coverage, employing additional sensors, or capturing large multi-view datasets. We instead propose a self-supervised solution, which exploits a key insight: localizing a query image within a map should yield the same absolute pose, regardless of the reference image used for registration. Guided by this intuition, we derive a novel transform consistency loss. Using this loss function, we train a deep neural network to infer dense feature and saliency maps to perform robust metric relocalization in dynamic environments. We evaluate our framework on synthetic and real-world data, showing our approach outperforms other supervised methods when a limited amount of ground-truth information is available.


Reviews and Rebuttal

Reviews & Rebuttal

Conference on Robot Learning 2020