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.

Cet article explore les excursions dans le temps et leurs implications.

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