  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.
