Unsupervised Metric Relocalization Using Transform Consistency Loss

• URL: http://arxiv.org/abs/2011.00608v1
• Date: Sun, 1 Nov 2020 19:24:27 GMT
• Title: Unsupervised Metric Relocalization Using Transform Consistency Loss
• Authors: Mike Kasper, Fernando Nobre, Christoffer Heckman, Nima Keivan
• Abstract summary: Training networks to perform metric relocalization traditionally requires accurate image correspondences. We 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. 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.
• Score: 66.19479868638925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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