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A major challenge in place recognition for autonomous driving is to be robustagainst appearance changes due to short-term (e.g., weather, lighting) andlong-term (seasons, vegetation growth, etc.) environmental variations. Apromising solution is to continuously accumulate images to maintain an adequatesample of the conditions and incorporate new changes into the place recognitiondecision. However, this demands a place recognition technique that is scalableon an ever growing dataset. To this end, we propose a novel place recognitiontechnique that can be efficiently retrained and compressed, such that therecognition of new queries can exploit all available data (including recentchanges) without suffering from visible growth in computational cost.Underpinning our method is a novel temporal image matching technique based onHidden Markov Models. Our experiments show that, compared to state-of-the-arttechniques, our method has much greater potential for large-scale placerecognition for autonomous driving.

Scalable Place Recognition Under Appearance Change for Autonomous Driving