HM4: Hidden Markov Model With Memory Management for Visual Place Recognition

Dzung Anh Doan, Yasir Latif, Tat-Jun Chin, Ian Reid

Abstract:

Visual place recognition needs to be robust against
appearance variability due to natural and man-made causes.
Training data collection should thus be an ongoing process to
allow continuous appearance changes to be recorded. However,
this creates an unboundedly-growing database that poses time
and memory scalability challenges for place recognition methods.
To tackle the scalability issue for visual place recognition in autonomous driving, we develop a Hidden Markov Model approach
with a two-tiered memory management. Our algorithm, dubbed
HM4, exploits temporal look-ahead to transfer promising candidate images between passive storage and active memory when
needed. The inference process takes into account both promising
images and a coarse representations of the full database. We show
that this allows constant time and space inference for a fixed
coverage area. The coarse representations can also be updated
incrementally to absorb new data. To further reduce the memory
requirements, we derive a compact image representation inspired
by Locality Sensitive Hashing (LSH). Through experiments on
real world data, we demonstrate the excellent scalability and
accuracy of the approach under appearance changes and provide
comparisons against state-of-the-art techniques