Laser-radar data fusion with Gaussian Process Implicit Surfaces

Gerardo-Castro, Marcos P., Peynot, Thierry, & Ramos, Fabio (2013) Laser-radar data fusion with Gaussian Process Implicit Surfaces. In Corke, Peter, Mejias, Luis, & Roberts, Jonathan (Eds.) FSR2013 : The 9th International Conference on Field and Service Robotics, Brisbane, Australia.


This work considers the problem of building high-fidelity 3D representations of the environment from sensor data acquired by mobile robots. Multi-sensor data fusion allows for more complete and accurate representations, and for more reliable perception, especially when different sensing modalities are used. In this paper, we propose a thorough experimental analysis of the performance of 3D surface reconstruction from laser and mm-wave radar data using Gaussian Process Implicit Surfaces (GPIS), in a realistic field robotics scenario. We first analyse the performance of GPIS using raw laser data alone and raw radar data alone, respectively, with different choices of covariance matrices and different resolutions of the input data. We then evaluate and compare the performance of two different GPIS fusion approaches. The first, state-of-the-art approach directly fuses raw data from laser and radar. The alternative approach proposed in this paper first computes an initial estimate of the surface from each single source of data, and then fuses these two estimates. We show that this method outperforms the state of the art, especially in situations where the sensors react differently to the targets they perceive.

Impact and interest:

1 citations in Web of Science®
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ID Code: 67603
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Laser range finder, radar, sensor fusion, mobile robots, Gaussian processes, Implicit surfaces
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Please consult the authors
Deposited On: 06 Mar 2014 00:18
Last Modified: 30 Apr 2014 18:18

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