| Seznam |
| Téma: | Visual navigation using artificial landmarks |
|---|---|
| Katedra: | Multirobotické systémy |
| Vedoucí: | Ing. Vojtěch Vonásek, Ph.D. |
| Vypsáno jako: | Diplomová práce, Bakalářská práce, Semestrální projekt |
| Popis: | The task of visual navigation is to derive robot paths based on a visual information (camera data) and using a known map of (visual) landmarks [1]. The task of this semestral work is to derive a simple navigation algorithm for a 2D mobile robot that uses only the visual information. Student should select a suitable map representation (see [2-4] for inspiration) for visual landmarks (e.g. a bar-code or other image features like SIFT, SURF, etc.) and implement a state-of-the-art method for robot navigation ([3] for inspiration). The project requires programming in c/c++/python. The result of a project is a functional code demonstrated by a video and technical report.
[1] S. Se, D. G. Lowe and J. J. Little, "Vision-based global localization and mapping for mobile robots," in IEEE Transactions on Robotics, vol. 21, no. 3, pp. 364-375, June 2005, doi: 10.1109/TRO.2004.839228. [2] Mirowski, P., Grimes, M. K., Malinowski, M., Hermann, K. M., Anderson, K., Teplyashin, D., ... & Hadsell, R. (2018). Learning to navigate in cities without a map. arXiv preprint arXiv:1804.00168. [3] Chen, K., de Vicente, J. P., Sepulveda, G., Xia, F., Soto, A., Vázquez, M., & Savarese, S. (2019). A behavioral approach to visual navigation with graph localization networks. arXiv preprint arXiv:1903.00445. [4] Qing Li, Jiasong Zhu, Tao Liu, Jon Garibaldi, Qingquan Li, and Guoping Qiu. 2017. Visual landmark sequence-based indoor localization. In Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery (GeoAI '17). Association for Computing Machinery, New York, NY, USA, 14–23. |