Biologically-inspired place recognition with neural networks

Chen, Zetao (2016) Biologically-inspired place recognition with neural networks. PhD by Publication, Queensland University of Technology.

[img] Zetao Chen Thesis (PDF 13MB)
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Abstract

This thesis explores two aspects of biologically inspired methods for place recognition, a key component of navigation. The first key theme is to explore the multi-scale mapping principles inspired by the recent discovery of overlapping, multi-scale spatial maps in the rodent brain, while the second develops biologically inspired Convolutional Neural Networks (CNNs) for place recognition. We presented a series of studies comprehensively demonstrating for the first time how both a rodent brain-inspired multi-scale mapping system and CNN-based techniques enable state of the art place recognition performance.

Impact and interest:

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ID Code: 98550
Item Type: QUT Thesis (PhD by Publication)
Supervisor: Milford, Michael, Wyeth, Gordon, & Corke, Peter
Keywords: Biologically inspired robotics, Place recognition, Robot localization, Long-term autonomy, SLAM, Convolutional neural network, Deep learning, Grid cells, Metric learning
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: Queensland University of Technology
Deposited On: 29 Jan 2017 23:45
Last Modified: 29 Jan 2017 23:45

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