Robust multiple-sensing-modality data fusion using Gaussian Process Implicit Surfaces

Gerardo-Castro, MP; Peynot, T; Ramos, F; Fitch, R

Robust multiple-sensing-modality data fusion using Gaussian Process Implicit Surfaces

Gerardo-Castro, MP Peynot, T Ramos, F Fitch, R

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Publication Type:: Conference Proceeding
Citation:: FUSION 2014 - 17th International Conference on Information Fusion, 2014
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Gerardo-Castro, MP	en_US
dc.contributor.author	Peynot, T	en_US
dc.contributor.author	Ramos, F	en_US
dc.contributor.author	Fitch, R https://orcid.org/0000-0003-2215-5188	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	FUSION 2014 - 17th International Conference on Information Fusion, 2014	en_US
dc.identifier.isbn	9788490123553	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121410
dc.description.abstract	© 2014 International Society of Information Fusion. The ability to build high-fidelity 3D representations of the environment from sensor data is critical for autonomous robots. Multi-sensor data fusion allows for more complete and accurate representations. Furthermore, using distinct sensing modalities (i.e. sensors using a different physical process and/or operating at different electromagnetic frequencies) usually leads to more reliable perception, especially in challenging environments, as modalities may complement each other. However, they may react differently to certain materials or environmental conditions, leading to catastrophic fusion. In this paper, we propose a new method to reliably fuse data from multiple sensing modalities, including in situations where they detect different targets. We first compute distinct continuous surface representations for each sensing modality, with uncertainty, using Gaussian Process Implicit Surfaces (GPIS). Second, we perform a local consistency test between these representations, to separate consistent data (i.e. data corresponding to the detection of the same target by the sensors) from inconsistent data. The consistent data can then be fused together, using another GPIS process, and the rest of the data can be combined as appropriate. The approach is first validated using synthetic data. We then demonstrate its benefit using a mobile robot, equipped with a laser scanner and a radar, which operates in an outdoor environment in the presence of large clouds of airborne dust and smoke.	en_US
dc.relation.ispartof	FUSION 2014 - 17th International Conference on Information Fusion	en_US
dc.title	Robust multiple-sensing-modality data fusion using Gaussian Process Implicit Surfaces	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	091303 Autonomous Vehicles	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/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 International Society of Information Fusion. The ability to build high-fidelity 3D representations of the environment from sensor data is critical for autonomous robots. Multi-sensor data fusion allows for more complete and accurate representations. Furthermore, using distinct sensing modalities (i.e. sensors using a different physical process and/or operating at different electromagnetic frequencies) usually leads to more reliable perception, especially in challenging environments, as modalities may complement each other. However, they may react differently to certain materials or environmental conditions, leading to catastrophic fusion. In this paper, we propose a new method to reliably fuse data from multiple sensing modalities, including in situations where they detect different targets. We first compute distinct continuous surface representations for each sensing modality, with uncertainty, using Gaussian Process Implicit Surfaces (GPIS). Second, we perform a local consistency test between these representations, to separate consistent data (i.e. data corresponding to the detection of the same target by the sensors) from inconsistent data. The consistent data can then be fused together, using another GPIS process, and the rest of the data can be combined as appropriate. The approach is first validated using synthetic data. We then demonstrate its benefit using a mobile robot, equipped with a laser scanner and a radar, which operates in an outdoor environment in the presence of large clouds of airborne dust and smoke.

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http://hdl.handle.net/10453/121410