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TAU-Indoors Dataset for Visual and LiDAR Place Recognition

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Image Analysis (SCIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13886))

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Abstract

There is a growing number of autonomous driving datasets that can be used to benchmark vision and LiDAR based place recognition and localization methods. The same sensor modalities, vision and depth, are important for indoor localization and navigation as well, but there is a lack of large indoor datasets. This work presents a realistic indoor dataset for long-term evaluation of place recognition and localization methods. The dataset contains RGB and LiDAR sequences captured inside campus buildings over a time period of nine months and in various illumination and occupancy conditions. The dataset contains three typical indoor spaces: office, basement and foyer. We describe collection of the dataset and in the experimental part we report results for the two state-of-the-art deep learning place recognition methods. The data will be available through https://github.com/lasuomela/TAU-Indoors.

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Authors and Affiliations

  1. Tampere University, Tampere, Finland

    Atakan Dag, Farid Alijani, Jukka Peltomaki, Lauri Suomela, Esa Rahtu, Harry Edelman & Joni-Kristian Kämäräinen

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  1. Atakan Dag
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  2. Farid Alijani
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  3. Jukka Peltomaki
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  4. Lauri Suomela
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  5. Esa Rahtu
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  6. Harry Edelman
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  7. Joni-Kristian Kämäräinen
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Corresponding author

Correspondence to Lauri Suomela .

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Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Rikke Gade

  2. Linköping University, Linköping, Sweden

    Michael Felsberg

  3. Tampere University, Tampere, Finland

    Joni-Kristian Kämäräinen

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Dag, A. et al. (2023). TAU-Indoors Dataset for Visual and LiDAR Place Recognition. In: Gade, R., Felsberg, M., Kämäräinen, JK. (eds) Image Analysis. SCIA 2023. Lecture Notes in Computer Science, vol 13886. Springer, Cham. https://doi.org/10.1007/978-3-031-31438-4_22

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  • DOI: https://doi.org/10.1007/978-3-031-31438-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31437-7

  • Online ISBN: 978-3-031-31438-4

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