LIDIA: Localizing in the Dark with Illumination-Awareness Toward Perception-Aware Planning

Abstract

Accurate Localization is a fundamental challenge in robotic autonomy, with applications ranging from autonomous driving to space proximity operations. Visual Localization is a viable choice in GPS-denied environments, such as subterranean, indoor, urban, or space environments; however, its performance degrades under often encountered conditions, such as low light or varying illumination. This paper introduces LIDIA, an illumination-aware model of localization quality for Perception-Aware Planning. LIDIA involves the efficient integration of light source direction into the planning framework, enabling the prediction of visually informative regions in the map under varying lighting. Unlike prior geometric approaches, LIDIA jointly exploits geometric and photometric information without requiring computationally expensive real-time rendering, thereby preserving online applicability. Our results demonstrate that LIDIA consistently outperforms existing geometric methods such as FIF in predicting the information gain of candidate camera poses and in planning trajectories that achieve higher localization accuracy. To the best of our knowledge, this is the first approach to unify geometric and photometric reasoning in an efficient, active localization system, paving the way for robust autonomy in illumination-constrained environments.