Learning Cross-Scale Visual Representations for Real-Time Image Geo-Localization

• URL: http://arxiv.org/abs/2109.04087v1
• Date: Thu, 9 Sep 2021 08:08:54 GMT
• Title: Learning Cross-Scale Visual Representations for Real-Time Image Geo-Localization
• Authors: Tianyi Zhang and Matthew Johnson-Roberson
• Abstract summary: State estimation approaches based on local sensors are drifting-prone for long-range missions as error accumulates. We introduce the cross-scale dataset and a methodology to produce additional data from cross-modality sources. We propose a framework that learns cross-scale visual representations without supervision.
• Score: 21.375640354558044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robot localization remains a challenging task in GPS denied environments. State estimation approaches based on local sensors, e.g. cameras or IMUs, are drifting-prone for long-range missions as error accumulates. In this study, we aim to address this problem by localizing image observations in a 2D multi-modal geospatial map. We introduce the cross-scale dataset and a methodology to produce additional data from cross-modality sources. We propose a framework that learns cross-scale visual representations without supervision. Experiments are conducted on data from two different domains, underwater and aerial. In contrast to existing studies in cross-view image geo-localization, our approach a) performs better on smaller-scale multi-modal maps; b) is more computationally efficient for real-time applications; c) can serve directly in concert with state estimation pipelines.

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