  A major challenge in place recognition for autonomous driving is to be robust
against appearance changes due to short-term (e.g., weather, lighting) and
long-term (seasons, vegetation growth, etc.) environmental variations. A
promising solution is to continuously accumulate images to maintain an adequate
sample of the conditions and incorporate new changes into the place recognition
decision. However, this demands a place recognition technique that is scalable
on an ever growing dataset. To this end, we propose a novel place recognition
technique that can be efficiently retrained and compressed, such that the
recognition of new queries can exploit all available data (including recent
changes) without suffering from visible growth in computational cost.
Underpinning our method is a novel temporal image matching technique based on
Hidden Markov Models. Our experiments show that, compared to state-of-the-art
techniques, our method has much greater potential for large-scale place
recognition for autonomous driving.
