Visual SLAM using Local Bundle Optimization in Unstructured Seafloor Environment
| Autor: | Seonghun Hong, Jinwhan Kim |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Scheme (programming language)
Basis (linear algebra) business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Context (language use) Simultaneous localization and mapping Bundle Computer vision Unmanned underwater vehicle Artificial intelligence business computer Curse of dimensionality Monocular camera Mathematics computer.programming_language |
| Zdroj: | The Journal of Korea Robotics Society. 9:197-205 |
| ISSN: | 1975-6291 |
| DOI: | 10.7746/jkros.2014.9.4.197 |
| Popis: | As computer vision algorithms are developed on a continuous basis, the visual information from vision sensors has been widely used in the context of simultaneous localization and mapping (SLAM), called visual SLAM, which utilizes relative motion information between images. This research addresses a visual SLAM framework for online localization and mapping in an unstructured seabed environment that can be applied to a low-cost unmanned underwater vehicle equipped with a single monocular camera as a major measurement sensor. Typically, an image motion model with a predefined dimensionality can be corrupted by errors due to the violation of the model assumptions, which may lead to performance degradation of the visual SLAM estimation. To deal with the erroneous image motion model, this study employs a local bundle optimization (LBO) scheme when a closed loop is detected. The results of comparison between visual SLAM estimation with LBO and the other case are presented to validate the effectiveness of the proposed methodology. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|