Neural Topological SLAM for Visual Navigation
| Autor: | Saurabh Gupta, Ruslan Salakhutdinov, Abhinav Gupta, Devendra Singh Chaplot |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer Science - Machine Learning Computer science Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 010501 environmental sciences Simultaneous localization and mapping Topology 01 natural sciences Machine Learning (cs.LG) Computer Science - Robotics 020901 industrial engineering & automation Leverage (statistics) 0105 earth and related environmental sciences business.industry Supervised learning Visual navigation Visualization Artificial Intelligence (cs.AI) Task analysis Artificial intelligence business Robotics (cs.RO) |
| Zdroj: | CVPR |
| DOI: | 10.48550/arxiv.2005.12256 |
| Popis: | This paper studies the problem of image-goal navigation which involves navigating to the location indicated by a goal image in a novel previously unseen environment. To tackle this problem, we design topological representations for space that effectively leverage semantics and afford approximate geometric reasoning. At the heart of our representations are nodes with associated semantic features, that are interconnected using coarse geometric information. We describe supervised learning-based algorithms that can build, maintain and use such representations under noisy actuation. Experimental study in visually and physically realistic simulation suggests that our method builds effective representations that capture structural regularities and efficiently solve long-horizon navigation problems. We observe a relative improvement of more than 50% over existing methods that study this task. Comment: Published in CVPR 2020. See the project webpage at https://devendrachaplot.github.io/projects/Neural-Topological-SLAM |
| Databáze: | OpenAIRE |
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