TAU-Indoors Dataset for Visual and LiDAR Place Recognition

Atakan Dag; Farid Alijani; Jukka Peltomäki; Lauri Suomela; Esa Rahtu; Harry Edelman; Joni Kristian Kämäräinen

doi:10.1007/978-3-031-31438-4_22

TAU-Indoors Dataset for Visual and LiDAR Place Recognition

Atakan Dag, Farid Alijani, Jukka Peltomäki, Lauri Suomela, Esa Rahtu, Harry Edelman, Joni Kristian Kämäräinen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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.

Original language	English
Title of host publication	Image Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings
Editors	Rikke Gade, Michael Felsberg, Joni-Kristian Kämäräinen
Publisher	Springer
Pages	326-339
Number of pages	14
ISBN (Electronic)	978-3-031-31438-4
ISBN (Print)	978-3-031-31437-7
DOIs	https://doi.org/10.1007/978-3-031-31438-4_22
Publication status	Published - 2023
Publication type	A4 Article in conference proceedings
Event	Scandinavian Conference on Image Analysis - Lapland, Finland Duration: 18 Apr 2023 → 21 Apr 2023

Publication series

Name	Lecture Notes in Computer Science
Volume	13886 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Scandinavian Conference on Image Analysis
Country/Territory	Finland
City	Lapland
Period	18/04/23 → 21/04/23

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-31438-4_22

Cite this

Dag, A., Alijani, F., Peltomäki, J., Suomela, L., Rahtu, E., Edelman, H., & Kämäräinen, J. K. (2023). TAU-Indoors Dataset for Visual and LiDAR Place Recognition. In R. Gade, M. Felsberg, & J.-K. Kämäräinen (Eds.), Image Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings (pp. 326-339). (Lecture Notes in Computer Science; Vol. 13886 LNCS). Springer. https://doi.org/10.1007/978-3-031-31438-4_22

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title = "TAU-Indoors Dataset for Visual and LiDAR Place Recognition",

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.",

author = "Atakan Dag and Farid Alijani and Jukka Peltom{\"a}ki and Lauri Suomela and Esa Rahtu and Harry Edelman and K{\"a}m{\"a}r{\"a}inen, {Joni Kristian}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; Scandinavian Conference on Image Analysis ; Conference date: 18-04-2023 Through 21-04-2023",

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doi = "10.1007/978-3-031-31438-4_22",

language = "English",

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Dag, A, Alijani, F, Peltomäki, J, Suomela, L , Rahtu, E, Edelman, H & Kämäräinen, JK 2023, TAU-Indoors Dataset for Visual and LiDAR Place Recognition. in R Gade, M Felsberg & J-K Kämäräinen (eds), Image Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings. Lecture Notes in Computer Science, vol. 13886 LNCS, Springer, pp. 326-339, Scandinavian Conference on Image Analysis, Lapland, Finland, 18/04/23. https://doi.org/10.1007/978-3-031-31438-4_22

TAU-Indoors Dataset for Visual and LiDAR Place Recognition. / Dag, Atakan; Alijani, Farid; Peltomäki, Jukka et al.
Image Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings. ed. / Rikke Gade; Michael Felsberg; Joni-Kristian Kämäräinen. Springer, 2023. p. 326-339 (Lecture Notes in Computer Science; Vol. 13886 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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T1 - TAU-Indoors Dataset for Visual and LiDAR Place Recognition

AU - Dag, Atakan

AU - Alijani, Farid

AU - Peltomäki, Jukka

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AU - Rahtu, Esa

AU - Edelman, Harry

AU - Kämäräinen, Joni Kristian

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AB - 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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