6D Camera Relocalization in Visually Ambiguous Extreme Environments
This addresses the problem of reliable camera pose estimation for autonomous vehicles in visually ambiguous extreme environments, representing an incremental improvement with domain-specific applications.
The paper tackles camera relocalization in extreme environments like deep seas or Mars-like deserts, where state-of-the-art methods fail due to textureless surfaces and ambiguous structures, and proposes a hierarchical localization system with environment-aware image enhancement, achieving superior performance in these settings and comparable results on an indoor benchmark with only 20% training data.
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.