A Hybrid Compact Neural Architecture for Visual Place Recognition
| Autor: | Luis Hernandez-Nunez, Andrew B. Barron, Ajay Narendra, Michael Milford, Marvin Chancán |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning 0209 industrial biotechnology Control and Optimization Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Biomedical Engineering 02 engineering and technology Spatial memory Machine Learning (cs.LG) Computer Science - Robotics 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Image retrieval Artificial neural network business.industry Mechanical Engineering Deep learning Pattern recognition Computer Science Applications Human-Computer Interaction Control and Systems Engineering Benchmark (computing) Key (cryptography) 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Robotics (cs.RO) |
| Zdroj: | IEEE Robotics and Automation Letters. 5:993-1000 |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2020.2967324 |
| Popis: | State-of-the-art algorithms for visual place recognition, and related visual navigation systems, can be broadly split into two categories: computer-science-oriented models including deep learning or image retrieval-based techniques with minimal biological plausibility, and neuroscience-oriented dynamical networks that model temporal properties underlying spatial navigation in the brain. In this letter, we propose a new compact and high-performing place recognition model that bridges this divide for the first time. Our approach comprises two key neural models of these categories: (1) FlyNet, a compact, sparse two-layer neural network inspired by brain architectures of fruit flies, Drosophila melanogaster, and (2) a one-dimensional continuous attractor neural network (CANN). The resulting FlyNet+CANN network incorporates the compact pattern recognition capabilities of our FlyNet model with the powerful temporal filtering capabilities of an equally compact CANN, replicating entirely in a hybrid neural implementation the functionality that yields high performance in algorithmic localization approaches like SeqSLAM. We evaluate our model, and compare it to three state-of-the-art methods, on two benchmark real-world datasets with small viewpoint variations and extreme environmental changes - achieving 87% AUC results under day to night transitions compared to 60% for Multi-Process Fusion, 46% for LoST-X and 1% for SeqSLAM, while being 6.5, 310, and 1.5 times faster, respectively. Preprint version of article published in IEEE Robotics and Automation Letters |
| Databáze: | OpenAIRE |
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