Prototyping vision-based localization using milestone-board information
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 prototype the use of milestone-board information for map-based localization. The investigation includes the following steps:
You will begin by first creating a scenario (i.e. as a Python or Matlab program, for example) that contains some highways, roads, cities, and virtual milestone boards.
You’ll then implement a particle filter (which is one of the most intuitive techniques for autonomous localization) for localization within the map (i.e. inside the created scenario). This also includes exploring multiple possible routes that lead to the same city.
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 prototype the use of milestone-board information for map-based localization. The investigation includes the following steps:
You will begin by first creating a scenario (i.e. as a Python or Matlab program, for example) that contains some highways, roads, cities, and virtual milestone boards.
You’ll then implement a particle filter (which is one of the most intuitive techniques for autonomous localization) for localization within the map (i.e. inside the created scenario). This also includes exploring multiple possible routes that lead to the same city.
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
Suhtlemiskeel(ed)
inglise keel
Nõuded kandideerijale
Tase
Bakalaureus
Märksõnad
Kandideerimise kontakt
Nimi
Naveed Muhammad
Tel
E-mail