IN2LAMA: INertial lidar localisation and mapping

Gentil, CL; Vidal-Calleja, T; Huang, S

IN2LAMA: INertial lidar localisation and mapping

Gentil, CL Vidal-Calleja, T

Huang, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2019, 2019-May pp. 6388 - 6394
Issue Date:: 2019-05-01

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Field	Value	Language
dc.contributor.author	Gentil, CL	en_US
dc.contributor.author	Vidal-Calleja, T https://orcid.org/0000-0002-5763-9644	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.date.available	2021-05-25T19:02:17Z
dc.date.issued	2019-05-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2019, 2019-May pp. 6388 - 6394	en_US
dc.identifier.isbn	9781538660263	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130552
dc.description.abstract	© 2019 IEEE. In this paper, we introduce a probabilistic framework for INertial Lidar Localisation And MApping (IN2LAMA). Most of today's lidars are based on spinning mechanisms that do not capture snapshots of the environment. As a result, movement of the sensor can occur while scanning. Without a good estimation of this motion, the resulting point clouds might be distorted. In the lidar mapping literature, a constant velocity motion model is commonly assumed. This is an approximation that does not necessarily always hold. The key idea of the proposed framework is to exploit preintegrated measurements over upsampled inertial data to handle motion distortion without the need for any explicit motion-model. It tightly integrates inertial and lidar data in a batch on-manifold optimisation formulation. Using temporally precise upsampled preintegrated measurement allows frame-to-frame planar and edge features association. Moreover, features are re-computed when the estimate of the state changes, consolidating front-end and back-end interaction. We validate the effectiveness of the approach through simulated and real data.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.relation.isbasedon	10.1109/ICRA.2019.8794429	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	IN2LAMA: INertial lidar localisation and mapping	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2019-May	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2019-May	en_US

Abstract:

© 2019 IEEE. In this paper, we introduce a probabilistic framework for INertial Lidar Localisation And MApping (IN2LAMA). Most of today's lidars are based on spinning mechanisms that do not capture snapshots of the environment. As a result, movement of the sensor can occur while scanning. Without a good estimation of this motion, the resulting point clouds might be distorted. In the lidar mapping literature, a constant velocity motion model is commonly assumed. This is an approximation that does not necessarily always hold. The key idea of the proposed framework is to exploit preintegrated measurements over upsampled inertial data to handle motion distortion without the need for any explicit motion-model. It tightly integrates inertial and lidar data in a batch on-manifold optimisation formulation. Using temporally precise upsampled preintegrated measurement allows frame-to-frame planar and edge features association. Moreover, features are re-computed when the estimate of the state changes, consolidating front-end and back-end interaction. We validate the effectiveness of the approach through simulated and real data.

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http://hdl.handle.net/10453/130552