Vision-Based Ego-Positioning for Internet-of-Vehicle
| Autor: | Chun-Hsin Wang, 王俊心 |
|---|---|
| Rok vydání: | 2015 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 103 This paper presents a method for ego-positioning with low cost monocular cameras for an IoV (Internet-of-Vehicle) system. To reduce the computational and memory requirements as well as the communication overheads, we formulate the model compression algorithm as a weighted k-cover problem for better preserving model structures. Specifically for real-world vision-based positioning applications, we consider the issues with large scene change and propose a model update algorithm to tackle these problems. A long-term positioning dataset with more than one month, 105 sessions, and 14,167 images is constructed. Based on both local and up-to-date models constructed in our approach, extensive experimental results show that sub-meter positioning accuracy can be achieved, which outperforms existing vision-based algorithms. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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