Long exposure localization in darkness using consumer cameras
This work addresses the challenge of reliable visual localization using low-cost cameras under extremely low-light conditions—two orders of magnitude darker than standard benchmarks—where prolonged exposure and high ISO introduce severe motion blur, degrading conventional methods. We systematically evaluate SeqSLAM’s robustness under such extreme blur. Methodologically, we acquire usable grayscale images via long-exposure (132–10,000 ms) and high-gain imaging, and enhance SeqSLAM’s blur tolerance through block-wise and local-neighborhood normalization. We provide the first mechanistic insight into SeqSLAM’s effectiveness under strong motion blur and empirically validate its cross-illumination and cross-perceptual-domain generalization—e.g., daytime training to nighttime localization. Experiments demonstrate stable localization in both synthetic and real-world ultra-low-light scenarios. Statistical analysis confirms that normalization is critical for maintaining robustness against motion blur.