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arXiv · 2410.12362

Human-Inspired Long-Term Indoor Localization in Human-Oriented Environment

Nicky Zimmerman·Matteo Sodano
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Abstract

Lifelong localization is crucial for enabling the autonomy of service robots. In this paper, we present an overview of our past research on long-term localization and mapping, exploiting geometric priors such as floor plans and integrating textual and semantic information. Our approach was validated on challenging sequences spanning over many months, and we released open source implementations.

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Nicky Zimmerman, Matteo Sodano. 2024-10-16. Human-Inspired Long-Term Indoor Localization in Human-Oriented Environment. https://arxiv.org/abs/2410.12362

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