An Iterated Extended Kalman Filter for 3D mapping via Kinect camera

Ling, L; Cheng, E; Burnett, IS

An Iterated Extended Kalman Filter for 3D mapping via Kinect camera

Ling, L Cheng, E

Burnett, IS

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Publication Type:: Conference Proceeding
Citation:: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2013, pp. 1773 - 1777
Issue Date:: 2013-10-18

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Field	Value	Language
dc.contributor.author	Ling, L	en_US
dc.contributor.author	Cheng, E https://orcid.org/0000-0003-1632-8062	en_US
dc.contributor.author	Burnett, IS https://orcid.org/0000-0003-3795-7722	en_US
dc.date.issued	2013-10-18	en_US
dc.identifier.citation	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2013, pp. 1773 - 1777	en_US
dc.identifier.isbn	9781479903566	en_US
dc.identifier.issn	1520-6149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120085
dc.description.abstract	This paper proposes the use of the Iterated Extended Kalman Filter (IEKF) in a real-time 3D mapping framework applied to Microsoft Kinect RGB-D data. Standard EKF techniques typically used for 3D mapping are susceptible to errors introduced during the state prediction linearization and measurement prediction. When models are highly nonlinear due to measurement errors e.g., outliers, occlusions and feature initialization errors, the errors propagate and directly result in divergence and estimation inconsistencies. To prevent linearized error propagation, this paper proposes repetitive linearization of the nonlinear measurement model to provide a running estimate of camera motion. The effects of iterated-EKF are experimentally simulated with synthetic map and landmark data on a range and bearing camera model. It was shown that the IEKF measurement update outperforms the EKF update when the state causes nonlinearities in the measurement function. In the real indoor environment 3D mapping experiment, more robust convergence behavior for the IEKF was demonstrated, whilst the EKF updates failed to converge. © 2013 IEEE.	en_US
dc.relation.ispartof	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICASSP.2013.6637957	en_US
dc.title	An Iterated Extended Kalman Filter for 3D mapping via Kinect camera	en_US
dc.type	Conference Proceeding
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	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 Professional Practice and Leadership
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper proposes the use of the Iterated Extended Kalman Filter (IEKF) in a real-time 3D mapping framework applied to Microsoft Kinect RGB-D data. Standard EKF techniques typically used for 3D mapping are susceptible to errors introduced during the state prediction linearization and measurement prediction. When models are highly nonlinear due to measurement errors e.g., outliers, occlusions and feature initialization errors, the errors propagate and directly result in divergence and estimation inconsistencies. To prevent linearized error propagation, this paper proposes repetitive linearization of the nonlinear measurement model to provide a running estimate of camera motion. The effects of iterated-EKF are experimentally simulated with synthetic map and landmark data on a range and bearing camera model. It was shown that the IEKF measurement update outperforms the EKF update when the state causes nonlinearities in the measurement function. In the real indoor environment 3D mapping experiment, more robust convergence behavior for the IEKF was demonstrated, whilst the EKF updates failed to converge. © 2013 IEEE.

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http://hdl.handle.net/10453/120085