Multi-Technique Sequential Information Consistency For Dynamic Visual Place Recognition In Changing Environments

• URL: http://arxiv.org/abs/2401.08263v1
• Date: Tue, 16 Jan 2024 10:35:01 GMT
• Title: Multi-Technique Sequential Information Consistency For Dynamic Visual Place Recognition In Changing Environments
• Authors: Bruno Arcanjo, Bruno Ferrarini, Michael Milford, Klaus D. McDonald-Maier and Shoaib Ehsan
• Abstract summary: Visual place recognition (VPR) is an essential component of robot navigation and localization systems. No single VPR technique excels in every environmental condition. We propose a VPR system dubbed Multi-Sequential Information Consistency (MuSIC)
• Score: 23.33092172788319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual place recognition (VPR) is an essential component of robot navigation and localization systems that allows them to identify a place using only image data. VPR is challenging due to the significant changes in a place's appearance driven by different daily illumination, seasonal weather variations and diverse viewpoints. Currently, no single VPR technique excels in every environmental condition, each exhibiting unique benefits and shortcomings, and therefore combining multiple techniques can achieve more reliable VPR performance. Present multi-method approaches either rely on online ground-truth information, which is often not available, or on brute-force technique combination, potentially lowering performance with high variance technique sets. Addressing these shortcomings, we propose a VPR system dubbed Multi-Sequential Information Consistency (MuSIC) which leverages sequential information to select the most cohesive technique on an online per-frame basis. For each technique in a set, MuSIC computes their respective sequential consistencies by analysing the frame-to-frame continuity of their top match candidates, which are then directly compared to select the optimal technique for the current query image. The use of sequential information to select between VPR methods results in an overall VPR performance increase across different benchmark datasets, while avoiding the need for extra ground-truth of the runtime environment.

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