IN2LAMA: INertial Lidar Localisation And Mapping

Le Gentil, C; Vidal Calleja, T; Huang, S

IN2LAMA: INertial Lidar Localisation And Mapping

Le Gentil, C

Vidal Calleja, T

Huang, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 International Conference on Robotics and Automation (ICRA), 2019, 2019-May, pp. 6388-6394
Issue Date:: 2019-05-20

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Field	Value	Language
dc.contributor.author	Le Gentil, C https://orcid.org/0000-0002-9790-5935
dc.contributor.author	Vidal Calleja, T https://orcid.org/0000-0002-5763-9644
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178
dc.contributor.editor	Howard, A
dc.contributor.editor	Althoefer, K
dc.contributor.editor	Arai, F
dc.contributor.editor	Arrichiello, F
dc.contributor.editor	Caputo, B
dc.contributor.editor	Castellanos, J
dc.contributor.editor	Hauser, K
dc.contributor.editor	Isler, V
dc.contributor.editor	Kim, J
dc.contributor.editor	Liu, H
dc.contributor.editor	Oh, P
dc.contributor.editor	Santos, V
dc.contributor.editor	Scaramuzza, D
dc.contributor.editor	Ude, A
dc.contributor.editor	Voyles, R
dc.contributor.editor	Yamane, K
dc.contributor.editor	Okamura, A
dc.date	2019-05-20
dc.date.accessioned	2020-04-26T07:54:06Z
dc.date.available	2020-04-26T07:54:06Z
dc.date.issued	2019-05-20
dc.identifier.citation	2019 International Conference on Robotics and Automation (ICRA), 2019, 2019-May, pp. 6388-6394
dc.identifier.isbn	978-1-5386-6027-0
dc.identifier.issn	1050-4729
dc.identifier.issn	2577-087X
dc.identifier.uri	http://hdl.handle.net/10453/140283
dc.description.abstract	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.
dc.language	en
dc.publisher	IEEE
dc.relation	DAAD GermanyDAAD 57434417
dc.relation.ispartof	2019 International Conference on Robotics and Automation (ICRA)
dc.relation.ispartof	International Conference on Robotics and Automation
dc.relation.isbasedon	10.1109/ICRA.2019.8794429
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	IN2LAMA: INertial Lidar Localisation And Mapping
dc.type	Conference Proceeding
utslib.citation.volume	2019-May
utslib.location.activity	Montreal
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Students
pubs.organisational-group	/University of Technology Sydney
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	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2020-04-26T07:54:02Z
pubs.finish-date	2019-05-24
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-05-20
pubs.volume	2019-May
utslib.start-page	6388
dc.location	Piscataway, USA

Abstract:

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/10453/140283