ICRA 2011 Paper Abstract

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Paper TuP1-InteracInterac.37

Vasudevan, Shrihari (University of Sydney), Ramos, Fabio (University of Sydney), Nettleton, Eric (The University of Sydney), Durrant-Whyte, Hugh (The University of Sydney)

Non-stationary dependent Gaussian processes for data fusion in large-scale terrain modeling

Scheduled for presentation during the Poster Sessions "Interactive Session II: Systems, Control and Automation" (TuP1-InteracInterac), Tuesday, May 10, 2011, 13:40−14:55, Hall

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Mining Robotics, Sensor Fusion, Learning and Adaptive Systems

Abstract

Obtaining a comprehensive model of large and complex terrain typically entails the use of both multiple sensory modalities and multiple data sets. This paper demonstrates the use of dependent Gaussian processes for data fusion in the context of large scale terrain modeling. Specifically, this paper derives and demonstrates the use of a non-stationary kernel (Neural Network) in this context. Experiments performed on multiple large scale (spanning about 5 sq km) 3D terrain data sets obtained from multiple sensory modalities (GPS surveys and laser scans) demonstrate the approach to data fusion and provide a preliminary demonstration of the superior modeling capability of Gaussian processes based on this kernel.

 

 

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