IDOL: A Framework for IMU-DVS Odometry using Lines

Gentil, CL; Tschopp, F; Alzugaray, I; Vidal-Calleja, T; Siegwart, R; Nieto, J

IDOL: A Framework for IMU-DVS Odometry using Lines

Gentil, CL Tschopp, F Alzugaray, I Vidal-Calleja, T Siegwart, R Nieto, J

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Publication Type:: Journal Article
Citation:: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, 00, pp. 5863-5870
Issue Date:: 2021-01-24

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Field	Value	Language
dc.contributor.author	Gentil, CL
dc.contributor.author	Tschopp, F
dc.contributor.author	Alzugaray, I
dc.contributor.author	Vidal-Calleja, T
dc.contributor.author	Siegwart, R
dc.contributor.author	Nieto, J
dc.date.accessioned	2021-03-10T05:34:22Z
dc.date.available	2021-03-10T05:34:22Z
dc.date.issued	2021-01-24
dc.identifier.citation	2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021, 00, pp. 5863-5870
dc.identifier.uri	http://hdl.handle.net/10453/146992
dc.description.abstract	In this paper, we introduce IDOL, an optimization-based framework for IMU-DVS Odometry using Lines. Event cameras, also called Dynamic Vision Sensors (DVSs), generate highly asynchronous streams of events triggered upon illumination changes for each individual pixel. This novel paradigm presents advantages in low illumination conditions and high-speed motions. Nonetheless, this unconventional sensing modality brings new challenges to perform scene reconstruction or motion estimation. The proposed method offers to leverage a continuous-time representation of the inertial readings to associate each event with timely accurate inertial data. The method's front-end extracts event clusters that belong to line segments in the environment whereas the back-end estimates the system's trajectory alongside the lines' 3D position by minimizing point-to-line distances between individual events and the lines' projection in the image space. A novel attraction/repulsion mechanism is presented to accurately estimate the lines' extremities, avoiding their explicit detection in the event data. The proposed method is benchmarked against a state-of-the-art frame-based visual-inertial odometry framework using public datasets. The results show that IDOL performs at the same order of magnitude on most datasets and even shows better orientation estimates. These findings can have a great impact on new algorithms for DVS.
dc.language	en
dc.relation	http://purl.org/au-research/grants/arc/DP210101336
dc.relation.ispartof	2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
dc.relation.isbasedon	10.1109/IROS45743.2020.9341208
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	IDOL: A Framework for IMU-DVS Odometry using Lines
dc.type	Journal Article
utslib.citation.volume	00
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Design
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	recently_added	*
dc.date.updated	2021-03-10T05:34:01Z
pubs.volume	00

Abstract:

In this paper, we introduce IDOL, an optimization-based framework for IMU-DVS Odometry using Lines. Event cameras, also called Dynamic Vision Sensors (DVSs), generate highly asynchronous streams of events triggered upon illumination changes for each individual pixel. This novel paradigm presents advantages in low illumination conditions and high-speed motions. Nonetheless, this unconventional sensing modality brings new challenges to perform scene reconstruction or motion estimation. The proposed method offers to leverage a continuous-time representation of the inertial readings to associate each event with timely accurate inertial data. The method's front-end extracts event clusters that belong to line segments in the environment whereas the back-end estimates the system's trajectory alongside the lines' 3D position by minimizing point-to-line distances between individual events and the lines' projection in the image space. A novel attraction/repulsion mechanism is presented to accurately estimate the lines' extremities, avoiding their explicit detection in the event data. The proposed method is benchmarked against a state-of-the-art frame-based visual-inertial odometry framework using public datasets. The results show that IDOL performs at the same order of magnitude on most datasets and even shows better orientation estimates. These findings can have a great impact on new algorithms for DVS.

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http://hdl.handle.net/10453/146992