  We propose a novel method to reliably estimate the pose of a camera given a
sequence of images acquired in extreme environments such as deep seas or
extraterrestrial terrains. Data acquired under these challenging conditions are
corrupted by textureless surfaces, image degradation, and presence of
repetitive and highly ambiguous structures. When naively deployed, the
state-of-the-art methods can fail in those scenarios as confirmed by our
empirical analysis. In this paper, we attempt to make camera relocalization
work in these extreme situations. To this end, we propose: (i) a hierarchical
localization system, where we leverage temporal information and (ii) a novel
environment-aware image enhancement method to boost the robustness and
accuracy. Our extensive experimental results demonstrate superior performance
in favor of our method under two extreme settings: localizing an autonomous
underwater vehicle and localizing a planetary rover in a Mars-like desert. In
addition, our method achieves comparable performance with state-of-the-art
methods on the indoor benchmark (7-Scenes dataset) using only 20% training
data.
