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• W3114336385 abstract "One of the main difficulties of scaling current localization systems to large environments is the on-board storage required for the maps. In this paper we propose to learn to compress the map representation such that it is optimal for the localization task. As a consequence, higher compression rates can be achieved without loss of localization accuracy when compared to standard coding schemes that optimize for reconstruction, thus ignoring the end task. Our experiments show that it is possible to learn a task-specific compression which reduces storage requirements by two orders of magnitude over general-purpose codecs such as WebP without sacrificing performance." @default.
• W3114336385 created "2021-01-05" @default.
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• W3114336385 date "2020-12-20" @default.
• W3114336385 modified "2023-09-25" @default.
• W3114336385 title "Learning to Localize Through Compressed Binary Maps" @default.
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• W3114336385 doi "https://doi.org/10.48550/arxiv.2012.10942" @default.
• W3114336385 hasPublicationYear "2020" @default.
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