Crossview geolocalization for autonomous driving
Organisatsiooni nimi
Autonomous Driving Lab
Kokkuvõte
Autonomous vehicles, like other robots, need to localize themselves in order to navigate. While satellite navigation systems (GNSS) such as GPS can provide such vehicles with localization information, the GNSS information might not always be available. This is where cross-view geolocalization comes in. It refers to performing localization using vehicle’s sensors (such as a camera) and using information from a completely different modality such as satellite images as a map of the environment that you are trying to localize the vehicle in.
Recently, many works have been published on cross-view geolocalization in urban environments such as cities, such as [1], [2], and [3].
In this project, you will:
Implement at least three methods for cross-view geolocalization (e.g. [1], [2], [3] or other similar recent methods) on a single dataset, in order to quantitatively compare the methods. Depending on the exact methods from the literature that you choose to compare, you are welcome to use the code from the authors of the studies in case it’s publicly available. You are encouraged to propose at least one improvement to one of the (compared) methods.
Incorporate one of the methods (preferably the method you proposed an improvement to) into our Autoware Mini software stack - https://adl.cs.ut.ee/lab/software
The results will also potentially be published in the form of a research paper.
Reference:
[1] L. M. Downes, T. J. Steiner, R. L. Russell and J. P. How, "Wide-Area Geolocalization with a Limited Field of View Camera," 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023, pp. 10594-10600, doi: 10.1109/ICRA48891.2023.10160607.
[2] L. M. Downes, D. -K. Kim, T. J. Steiner and J. P. How, "City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent," 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 11102-11108, doi: 10.1109/IROS47612.2022.9981996.
[3] S. Wang, Y. Zhang, A. Vora, A. Perincherry and H. Li, "Satellite Image Based Cross-view Localization for Autonomous Vehicle," 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023, pp. 3592-3599, doi: 10.1109/ICRA48891.2023.10161527.
Recently, many works have been published on cross-view geolocalization in urban environments such as cities, such as [1], [2], and [3].
In this project, you will:
Implement at least three methods for cross-view geolocalization (e.g. [1], [2], [3] or other similar recent methods) on a single dataset, in order to quantitatively compare the methods. Depending on the exact methods from the literature that you choose to compare, you are welcome to use the code from the authors of the studies in case it’s publicly available. You are encouraged to propose at least one improvement to one of the (compared) methods.
Incorporate one of the methods (preferably the method you proposed an improvement to) into our Autoware Mini software stack - https://adl.cs.ut.ee/lab/software
The results will also potentially be published in the form of a research paper.
Reference:
[1] L. M. Downes, T. J. Steiner, R. L. Russell and J. P. How, "Wide-Area Geolocalization with a Limited Field of View Camera," 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023, pp. 10594-10600, doi: 10.1109/ICRA48891.2023.10160607.
[2] L. M. Downes, D. -K. Kim, T. J. Steiner and J. P. How, "City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent," 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 11102-11108, doi: 10.1109/IROS47612.2022.9981996.
[3] S. Wang, Y. Zhang, A. Vora, A. Perincherry and H. Li, "Satellite Image Based Cross-view Localization for Autonomous Vehicle," 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023, pp. 3592-3599, doi: 10.1109/ICRA48891.2023.10161527.
Lõputöö kaitsmise aasta
2023-2024
Juhendaja
Syeda Zillay Nain Zukhraf, Naveed Muhammad
Suhtlemiskeel(ed)
inglise keel
Nõuded kandideerijale
Tase
Magister
Märksõnad
Kandideerimise kontakt
Nimi
Naveed Muhammad
Tel
E-mail