Automatic Visual Bag-of-Words for Online Robot Navigation and Mapping
Detecting already-visited regions based on their visual appearance helps reduce drift and position uncertainties in robot navigation and mapping. Inspired from content-based image retrieval, an efficient approach is the use of visual vocabularies to measure similarities between images. This way, ima...
| Authors: | ; |
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| Format: | Article |
| Status: | Published version |
| Publication date: | 2012 |
| Country: | España |
| Institution: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repository: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/8647 |
| Online Access: | http://hdl.handle.net/10256/8647 |
| Access Level: | Embargoed access |
| Keyword: | Imatges -- Processament Image processing Visió per ordinador Computer vision |
| Summary: | Detecting already-visited regions based on their visual appearance helps reduce drift and position uncertainties in robot navigation and mapping. Inspired from content-based image retrieval, an efficient approach is the use of visual vocabularies to measure similarities between images. This way, images corresponding to the same scene region can be associated. State-of-theart proposals that address this topic use prebuilt vocabularies that generally require a priori knowledge of the environment. We propose a novel method for appearance-based navigation and mapping where the visual vocabularies are built online, thus eliminating the need for prebuilt data. We also show that the proposed technique allows efficient loop-closure detection, even at small vocabulary sizes, resulting in a higher computational efficiency |
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