Scalable Place Recognition Under Appearance Change for Autonomous Driving

Doan, Anh-Dzung, Latif, Yasir, Chin, Tat-Jun, Liu, Yu, Do, Thanh-Toan, Reid, Ian and IEEE, (2020) Scalable Place Recognition Under Appearance Change for Autonomous Driving. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019-10-27 - 2019-11-2.

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Official URL: http://dx.doi.org/10.1109/iccv.2019.00941

Abstract

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.

Item Type:	Conference or Workshop Item (Unspecified)
Depositing User:	Symplectic Admin
Date Deposited:	10 Sep 2020 12:05
Last Modified:	18 Jan 2023 23:34
DOI:	10.1109/ICCV.2019.00941
Open Access URL:	https://arxiv.org/pdf/1908.00178.pdf
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3100665