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^*

^*Corresponding author for this work

Edinburgh Napier University
Royal Liverpool University Hospital
Monash University

Research output: Chapter in Book/Published conference output › Conference publication

9 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
Publisher	IEEE
Pages	3845-3850
Number of pages	6
ISBN (Electronic)	9781665479271
DOIs	https://doi.org/10.1109/IROS47612.2022.9981865
Publication status	Published - 26 Dec 2022
Event	2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 - Kyoto, Japan Duration: 23 Oct 2022 → 27 Oct 2022

Publication series

Name	IEEE International Conference on Intelligent Robots and Systems
Volume	2022-October
ISSN (Print)	2153-0858
ISSN (Electronic)	2153-0866

Conference

Conference	2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
Country/Territory	Japan
City	Kyoto
Period	23/10/22 → 27/10/22

Bibliographical note

Funding Information:
This work was partially supported by Amazon Web Services via the Oxford-Singapore Human-Machine Collaboration Programme and EPSRC ACE-OPS (EP/S030832/1)

Funding

∗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]

Access to Document

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 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. https://doi.org/10.1109/IROS47612.2022.9981865

@inproceedings{15414fe2e1b444cb931cac5305ccc019,

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: This work was partially supported by Amazon Web Services via the Oxford-Singapore Human-Machine Collaboration Programme and EPSRC ACE-OPS (EP/S030832/1) ; 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022 ; Conference date: 23-10-2022 Through 27-10-2022",

year = "2022",

month = dec,

day = "26",

doi = "10.1109/IROS47612.2022.9981865",

language = "English",

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

publisher = "IEEE",

pages = "3845--3850",

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

address = "United States",

}

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 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022. IEEE International Conference on Intelligent Robots and Systems, vol. 2022-October, IEEE, pp. 3845-3850, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 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. IEEE, 2022. p. 3845-3850 (IEEE International Conference on Intelligent Robots and Systems; Vol. 2022-October).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - OdomBeyondVision: An Indoor Multi-modal Multi-platform Odometry Dataset Beyond the Visible Spectrum

AU - Li, Peize

AU - Cai, Kaiwen

AU - Saputra, Muhamad Risqi U.

AU - Dai, Zhuangzhuang

AU - Lu, Chris Xiaoxuan

N1 - Funding Information: This work was partially supported by Amazon Web Services via the Oxford-Singapore Human-Machine Collaboration Programme and EPSRC ACE-OPS (EP/S030832/1)

PY - 2022/12/26

Y1 - 2022/12/26

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.

UR - https://www.scopus.com/pages/publications/85146341860

UR - https://ieeexplore.ieee.org/document/9981865

U2 - 10.1109/IROS47612.2022.9981865

DO - 10.1109/IROS47612.2022.9981865

M3 - Conference publication

AN - SCOPUS:85146341860

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

PB - IEEE

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

Y2 - 23 October 2022 through 27 October 2022

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 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022. IEEE. 2022. p. 3845-3850. (IEEE International Conference on Intelligent Robots and Systems). Epub 2022 Oct 23. doi: 10.1109/IROS47612.2022.9981865

OdomBeyondVision: An Indoor Multi-modal Multi-platform Odometry Dataset Beyond the Visible Spectrum

Abstract

Publication series

Conference

Bibliographical note

Funding

Access to Document

Other files and links

Fingerprint

Cite this