On Model-Free Re-ranking for Visual Place Recognition with Deep Learned Local Features

• URL: http://arxiv.org/abs/2410.18573v2
• Date: Fri, 25 Oct 2024 09:59:36 GMT
• Title: On Model-Free Re-ranking for Visual Place Recognition with Deep Learned Local Features
• Authors: Tomáš Pivoňka, Libor Přeučil,
• Abstract summary: Article focuses on model-free re-ranking based on standard local visual features. It introduces three new model-free re-ranking methods that were designed primarily for deep-learned local visual features.
• Score: 0.0
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• Abstract: Re-ranking is the second stage of a visual place recognition task, in which the system chooses the best-matching images from a pre-selected subset of candidates. Model-free approaches compute the image pair similarity based on a spatial comparison of corresponding local visual features, eliminating the need for computationally expensive estimation of a model describing transformation between images. The article focuses on model-free re-ranking based on standard local visual features and their applicability in long-term autonomy systems. It introduces three new model-free re-ranking methods that were designed primarily for deep-learned local visual features. These features evince high robustness to various appearance changes, which stands as a crucial property for use with long-term autonomy systems. All the introduced methods were employed in a new visual place recognition system together with the D2-net feature detector (Dusmanu, 2019) and experimentally tested with diverse, challenging public datasets. The obtained results are on par with current state-of-the-art methods, affirming that model-free approaches are a viable and worthwhile path for long-term visual place recognition.

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