期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 7, 期 2, 页码 5087-5094出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2022.3154035
关键词
Deep learning for visual perception; marine robotics; representation learning
类别
This study proposes a method to address the challenging task of robot localization in GPS denied environments by localizing image observations in a 2D multimodal geospatial map. The experiments show that the proposed method performs better on smaller-scale multimodal maps, is more computationally efficient for real-time applications, and can be used directly in concert with state estimation pipelines.
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 multimodal geospatial map. We introduce the cross-scale(1) 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.(2)
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