# Unsupervised Metric Relocalization Using Transform Consistency Loss

arXiv: https://arxiv.org/abs/2011.00608 · HTML: https://mlsanity.com/paper/2011.00608/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Self-Supervised Feature Learning for Long-Term Metric Visual Localization](https://mlsanity.com/paper/2212.00122/) (2022)

## Abstract (arXiv)

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.