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Gaussian process models for sensor-centric robot localisation

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3 Author(s)
Brooks, A. ; Australian Centre for Field Robotics, Sydney Univ., NSW ; Makarenko, A. ; Upcroft, B.

This paper presents an approach to building an observation likelihood function from a set of sparse, noisy training observations taken from known locations by a sensor with no obvious geometric model. The basic approach is to fit an interpolant to the training data, representing the expected observation, and to assume additive sensor noise. This paper takes a Bayesian view of the problem, maintaining a posterior over interpolants rather than simply the maximum-likelihood interpolant, giving a measure of uncertainty in the map at any point. This is done using a Gaussian process framework. To validate the approach experimentally, a model of an environment is built using observations from an omni-directional camera. After a model has been built from the training data, a particle filter is used to localise while traversing this environment

Published in:

Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on

Date of Conference:

15-19 May 2006