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

Gerardo-Castro, Marcos P., Peynot, Thierry, Ramos, Fabio, & Fitch, Robert (2014) Robust multiple-sensing-modality data fusion using Gaussian Process Implicit Surfaces. In 17th International Conference on Information Fusion (FUSION 2014), IEEE, Salamanca, Spain.


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.

Impact and interest:

2 citations in Scopus
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ID Code: 74586
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Robotics, Sensor Data Fusion, Laser, Radar, Gaussian Process Implicit Surfaces
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Please consult the authors
Deposited On: 31 Jul 2014 22:41
Last Modified: 22 Jun 2017 19:26

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