In this paper we evaluate performance of the SeqSLAM algorithm for passive vision-based localization in very dark environments with low-cost cameras that result in massively blurred images. We evaluate the effect of motion blur from exposure times up to 10,000 ms from a moving car, and the performance of localization in day time from routes learned at night in two different environments. Finally we perform a statistical analysis that compares the baseline performance of matching unprocessed grayscale images to using patch normalization and local neighborhood normalization - the two key SeqSLAM components. Our results and analysis show for the first time why the SeqSLAM algorithm is effective, and demonstrate the potential for cheap camera-based localization systems that function despite extreme appearance change.
@article{arxiv.2504.16406,
title = {Long Exposure Localization in Darkness Using Consumer Cameras},
author = {Michael Milford and Ian Turner and Peter Corke},
journal= {arXiv preprint arXiv:2504.16406},
year = {2025}
}