Vehicle localization in OpenStreetMap using distances to cities as the measurements
Organisatsiooni nimi
Autonomous Driving Lab
Kokkuvõte
Context: 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. One robust technique for vehicles to localize is using particle filters (e.g. [1], [2], [3]), given a map of the environment.
The project: During the project, you will investigate the use of distances to cities, essentially milestone board information, to implement map-based-localization in Open Street Map for Estonia. The investigation includes the following steps:
(i) You will begin by familiarizing yourself with OpenStreetMap and creating virtual milestone boards on a set of testing routes.
(ii) You’ll then implement a particle filter (which is one of the most intuitive techniques for autonomous localization) for localization with the map, using the information from the milestone boards as your measurements.
Depending on the depth and maturity of your investigation, the results may also be published in the form of a research paper.
Some relevant literature:
[1] J. Levinson and S. Thrun, "Robust vehicle localization in urban environments using probabilistic maps," 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010, pp. 4372-4378, doi: 10.1109/ROBOT.2010.5509700.
[2] Jesse Levinson, Michael Montemerlo, Sebastian Thrun, “Map-Based Precision Vehicle Localization in Urban Environments”, Proceedings of Robotics: Science and Systems, 2007.
[3] Philipp Ruchti, Bastian Steder, Michael Ruhnke, Wolfram Burgard, “Localization on OpenStreetMap Data using a 3D Laser Scanner”, Proceedings of International Conference on Robotics and Automation (ICRA), 2015.
The project: During the project, you will investigate the use of distances to cities, essentially milestone board information, to implement map-based-localization in Open Street Map for Estonia. The investigation includes the following steps:
(i) You will begin by familiarizing yourself with OpenStreetMap and creating virtual milestone boards on a set of testing routes.
(ii) You’ll then implement a particle filter (which is one of the most intuitive techniques for autonomous localization) for localization with the map, using the information from the milestone boards as your measurements.
Depending on the depth and maturity of your investigation, the results may also be published in the form of a research paper.
Some relevant literature:
[1] J. Levinson and S. Thrun, "Robust vehicle localization in urban environments using probabilistic maps," 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010, pp. 4372-4378, doi: 10.1109/ROBOT.2010.5509700.
[2] Jesse Levinson, Michael Montemerlo, Sebastian Thrun, “Map-Based Precision Vehicle Localization in Urban Environments”, Proceedings of Robotics: Science and Systems, 2007.
[3] Philipp Ruchti, Bastian Steder, Michael Ruhnke, Wolfram Burgard, “Localization on OpenStreetMap Data using a 3D Laser Scanner”, Proceedings of International Conference on Robotics and Automation (ICRA), 2015.
Lõputöö kaitsmise aasta
2024-2025
Juhendaja
Naveed Muhammad, Syeda Zillay Nain Zukhraf
Suhtlemiskeel(ed)
inglise keel
Nõuded kandideerijale
Tase
Magister
Märksõnad
Kandideerimise kontakt
Nimi
Naveed Muhammad
Tel
E-mail