OdomBeyondVision: An indoor multi-modal multi-platform odometry dataset beyond the visible spectrum

Peize Li; Kaiwen Cai; Muhamad Risqi U. Saputra; Zhuangzhuang Dai; Chris Xiaoxuan Lu

doi:10.1109/IROS47612.2022.9981865

OdomBeyondVision: An indoor multi-modal multi-platform odometry dataset beyond the visible spectrum

Peize Li, Kaiwen Cai, Muhamad Risqi U. Saputra, Zhuangzhuang Dai, Chris Xiaoxuan Lu

Data Science Indonesia

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

5 Citations (Scopus)

Abstract

This paper presents a multimodal indoor odometry dataset, OdomBeyondVision, featuring multiple sensors across the different spectrum and collected with different mobile platforms. Not only does OdomBeyondVision contain the traditional navigation sensors, sensors such as IMUs, mechanical LiDAR, RGBD camera, it also includes several emerging sensors such as the single-chip mmWave radar, LWIR thermal camera and solid-state LiDAR. With the above sensors on UAV, UGV and handheld platforms, we respectively recorded the multimodal odometry data and their movement trajectories in various indoor scenes and different illumination conditions. We release the exemplar radar, radar-inertial and thermal-inertial odometry implementations to demonstrate their results for future works to compare against and improve upon. The full dataset including toolkit and documentation is publicly available at: https://github.com/MAPS-Lab/OdomBeyondVision.

Original language	English
Title of host publication	IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
Editors	Zhidong Wang
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	3845-3850
Number of pages	6
ISBN (Electronic)	9781665479271
ISBN (Print)	9781665479288
DOIs	https://doi.org/10.1109/IROS47612.2022.9981865
Publication status	Published - 2022
Event	IEEE/RSJ International Conference on Intelligent Robots and Systems 2022 - Kyoto, Japan Duration: 23 Oct 2022 → 27 Oct 2022 https://ieeexplore.ieee.org/xpl/conhome/9981026/proceeding (Proceedings) https://iros2022.org/ (Website)

Publication series

Name	IEEE International Conference on Intelligent Robots and Systems
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Volume	2022-October
ISSN (Print)	2153-0858
ISSN (Electronic)	2153-0866

Conference

Conference	IEEE/RSJ International Conference on Intelligent Robots and Systems 2022
Abbreviated title	IROS 2022
Country/Territory	Japan
City	Kyoto
Period	23/10/22 → 27/10/22
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9981026/proceeding (Proceedings) https://iros2022.org/ (Website)

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10.1109/IROS47612.2022.9981865

Cite this

Li, P., Cai, K., Saputra, M. R. U., Dai, Z., & Lu, C. X. (2022). OdomBeyondVision: An indoor multi-modal multi-platform odometry dataset beyond the visible spectrum. In Z. Wang (Ed.), IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 (pp. 3845-3850). (IEEE International Conference on Intelligent Robots and Systems; Vol. 2022-October). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IROS47612.2022.9981865

Li, Peize ; Cai, Kaiwen ; Saputra, Muhamad Risqi U. et al. / OdomBeyondVision : An indoor multi-modal multi-platform odometry dataset beyond the visible spectrum. IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022. editor / Zhidong Wang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2022. pp. 3845-3850 (IEEE International Conference on Intelligent Robots and Systems).

@inproceedings{dc37f20aba834d65ae20db860c3b234c,

title = "OdomBeyondVision: An indoor multi-modal multi-platform odometry dataset beyond the visible spectrum",

abstract = "This paper presents a multimodal indoor odometry dataset, OdomBeyondVision, featuring multiple sensors across the different spectrum and collected with different mobile platforms. Not only does OdomBeyondVision contain the traditional navigation sensors, sensors such as IMUs, mechanical LiDAR, RGBD camera, it also includes several emerging sensors such as the single-chip mmWave radar, LWIR thermal camera and solid-state LiDAR. With the above sensors on UAV, UGV and handheld platforms, we respectively recorded the multimodal odometry data and their movement trajectories in various indoor scenes and different illumination conditions. We release the exemplar radar, radar-inertial and thermal-inertial odometry implementations to demonstrate their results for future works to compare against and improve upon. The full dataset including toolkit and documentation is publicly available at: https://github.com/MAPS-Lab/OdomBeyondVision.",

author = "Peize Li and Kaiwen Cai and Saputra, {Muhamad Risqi U.} and Zhuangzhuang Dai and Lu, {Chris Xiaoxuan}",

note = "Funding Information: ∗Corresponding author This work was partially supported by Amazon Web Services via the Oxford-Singapore Human-Machine Collaboration Programme and EPSRC ACE-OPS (EP/S030832/1) 1Peize Li and Chris Xiaoxuan Lu are with the School of Informatics, University of Edinburgh, United Kingdom. [email protected], [email protected] 2Kaiwen Cai is with University of Liverpool, United Kingdom. [email protected] 3Muhamad Risqi U. Saputra is with Monash University, Indonesia. [email protected] 4Zhuangzhuang Dai is with University of Oxford, United Kingdom. [email protected] Publisher Copyright: {\textcopyright} 2022 IEEE.; IEEE/RSJ International Conference on Intelligent Robots and Systems 2022, IROS 2022 ; Conference date: 23-10-2022 Through 27-10-2022",

year = "2022",

doi = "10.1109/IROS47612.2022.9981865",

language = "English",

isbn = "9781665479288",

series = "IEEE International Conference on Intelligent Robots and Systems",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "3845--3850",

editor = "Zhidong Wang",

booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022",

address = "United States of America",

url = "https://ieeexplore.ieee.org/xpl/conhome/9981026/proceeding, https://iros2022.org/",

}

Li, P, Cai, K, Saputra, MRU, Dai, Z & Lu, CX 2022, OdomBeyondVision: An indoor multi-modal multi-platform odometry dataset beyond the visible spectrum. in Z Wang (ed.), IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022. IEEE International Conference on Intelligent Robots and Systems, vol. 2022-October, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 3845-3850, IEEE/RSJ International Conference on Intelligent Robots and Systems 2022, Kyoto, Japan, 23/10/22. https://doi.org/10.1109/IROS47612.2022.9981865

OdomBeyondVision: An indoor multi-modal multi-platform odometry dataset beyond the visible spectrum. / Li, Peize; Cai, Kaiwen; Saputra, Muhamad Risqi U. et al.
IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022. ed. / Zhidong Wang. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2022. p. 3845-3850 (IEEE International Conference on Intelligent Robots and Systems; Vol. 2022-October).

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

TY - GEN

T1 - OdomBeyondVision

T2 - IEEE/RSJ International Conference on Intelligent Robots and Systems 2022

AU - Li, Peize

AU - Cai, Kaiwen

AU - Saputra, Muhamad Risqi U.

AU - Dai, Zhuangzhuang

AU - Lu, Chris Xiaoxuan

N1 - Funding Information: ∗Corresponding author This work was partially supported by Amazon Web Services via the Oxford-Singapore Human-Machine Collaboration Programme and EPSRC ACE-OPS (EP/S030832/1) 1Peize Li and Chris Xiaoxuan Lu are with the School of Informatics, University of Edinburgh, United Kingdom. [email protected], [email protected] 2Kaiwen Cai is with University of Liverpool, United Kingdom. [email protected] 3Muhamad Risqi U. Saputra is with Monash University, Indonesia. [email protected] 4Zhuangzhuang Dai is with University of Oxford, United Kingdom. [email protected] Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - This paper presents a multimodal indoor odometry dataset, OdomBeyondVision, featuring multiple sensors across the different spectrum and collected with different mobile platforms. Not only does OdomBeyondVision contain the traditional navigation sensors, sensors such as IMUs, mechanical LiDAR, RGBD camera, it also includes several emerging sensors such as the single-chip mmWave radar, LWIR thermal camera and solid-state LiDAR. With the above sensors on UAV, UGV and handheld platforms, we respectively recorded the multimodal odometry data and their movement trajectories in various indoor scenes and different illumination conditions. We release the exemplar radar, radar-inertial and thermal-inertial odometry implementations to demonstrate their results for future works to compare against and improve upon. The full dataset including toolkit and documentation is publicly available at: https://github.com/MAPS-Lab/OdomBeyondVision.

AB - This paper presents a multimodal indoor odometry dataset, OdomBeyondVision, featuring multiple sensors across the different spectrum and collected with different mobile platforms. Not only does OdomBeyondVision contain the traditional navigation sensors, sensors such as IMUs, mechanical LiDAR, RGBD camera, it also includes several emerging sensors such as the single-chip mmWave radar, LWIR thermal camera and solid-state LiDAR. With the above sensors on UAV, UGV and handheld platforms, we respectively recorded the multimodal odometry data and their movement trajectories in various indoor scenes and different illumination conditions. We release the exemplar radar, radar-inertial and thermal-inertial odometry implementations to demonstrate their results for future works to compare against and improve upon. The full dataset including toolkit and documentation is publicly available at: https://github.com/MAPS-Lab/OdomBeyondVision.

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DO - 10.1109/IROS47612.2022.9981865

M3 - Conference Paper

AN - SCOPUS:85146341860

SN - 9781665479288

T3 - IEEE International Conference on Intelligent Robots and Systems

SP - 3845

EP - 3850

BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022

A2 - Wang, Zhidong

PB - IEEE, Institute of Electrical and Electronics Engineers

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ER -

Li P, Cai K, Saputra MRU, Dai Z, Lu CX. OdomBeyondVision: An indoor multi-modal multi-platform odometry dataset beyond the visible spectrum. In Wang Z, editor, IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2022. p. 3845-3850. (IEEE International Conference on Intelligent Robots and Systems). doi: 10.1109/IROS47612.2022.9981865

OdomBeyondVision: An indoor multi-modal multi-platform odometry dataset beyond the visible spectrum

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