Biologically-inspired place recognition with neural networks
Chen, Zetao (2016) Biologically-inspired place recognition with neural networks. PhD by Publication, Queensland University of Technology.
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Zetao Chen Thesis (PDF 13MB) |
Description
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 |
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| 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 |
| DOI: | 10.5204/thesis.eprints.98550 |
| Divisions: | Past > QUT Faculties & Divisions > Science & Engineering Faculty Past > Schools > School of Electrical Engineering & Computer Science |
| Institution: | Queensland University of Technology |
| Deposited On: | 30 Jan 2017 09:45 |
| Last Modified: | 18 Jan 2025 00:43 |
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