Combining multiple sensor modalities for a localisation robust to smoke

Brunner, C; Peynot, T; Vidal-Calleja, T

Combining multiple sensor modalities for a localisation robust to smoke

Brunner, C Peynot, T Vidal-Calleja, T

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Intelligent Robots and Systems, 2011, pp. 2489 - 2496
Issue Date:: 2011-12-29

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Field	Value	Language
dc.contributor.author	Brunner, C	en_US
dc.contributor.author	Peynot, T	en_US
dc.contributor.author	Vidal-Calleja, T https://orcid.org/0000-0002-5763-9644	en_US
dc.date.issued	2011-12-29	en_US
dc.identifier.citation	IEEE International Conference on Intelligent Robots and Systems, 2011, pp. 2489 - 2496	en_US
dc.identifier.isbn	9781612844541	en_US
dc.identifier.uri	http://hdl.handle.net/10453/23603
dc.description.abstract	This paper proposes an approach to obtain a localisation that is robust to smoke by exploiting multiple sensing modalities: visual and infrared (IR) cameras. This localisation is based on a state-of-the-art visual SLAM algorithm. First, we show that a reasonably accurate localisation can be obtained in the presence of smoke by using only an IR camera, a sensor that is hardly affected by smoke, contrary to a visual camera (operating in the visible spectrum). Second, we demonstrate that improved results can be obtained by combining the information from the two sensor modalities (visual and IR cameras). Third, we show that by detecting the impact of smoke on the visual images using a data quality metric, we can anticipate and mitigate the degradation in performance of the localisation by discarding the most affected data. The experimental validation presents multiple trajectories estimated by the various methods considered, all thoroughly compared to an accurate dGPS/INS reference. © 2011 IEEE.	en_US
dc.relation.ispartof	IEEE International Conference on Intelligent Robots and Systems	en_US
dc.relation.isbasedon	10.1109/IROS.2011.6048181	en_US
dc.title	Combining multiple sensor modalities for a localisation robust to smoke	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0913 Mechanical Engineering	en_US
dc.location.activity	San Francisco, CA	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper proposes an approach to obtain a localisation that is robust to smoke by exploiting multiple sensing modalities: visual and infrared (IR) cameras. This localisation is based on a state-of-the-art visual SLAM algorithm. First, we show that a reasonably accurate localisation can be obtained in the presence of smoke by using only an IR camera, a sensor that is hardly affected by smoke, contrary to a visual camera (operating in the visible spectrum). Second, we demonstrate that improved results can be obtained by combining the information from the two sensor modalities (visual and IR cameras). Third, we show that by detecting the impact of smoke on the visual images using a data quality metric, we can anticipate and mitigate the degradation in performance of the localisation by discarding the most affected data. The experimental validation presents multiple trajectories estimated by the various methods considered, all thoroughly compared to an accurate dGPS/INS reference. © 2011 IEEE.

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http://hdl.handle.net/10453/23603