Vision-based localization on city scale using Open Street Map
Organization
Autonomous Driving Lab
Abstract
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.
During the project, you will investigate the use of vision sensing for map-based localization. The investigation includes the following steps:
1. Recognition of street names (and possibly also road direction signs) using vision
2. Matching of the perceived street-name information with streets in a given map (such as the Open Street Map)
3. Implementing a particle filter for localization within the map using the above-mentioned matches
4. Incorporation of the developed solution into the Autoware Mini software stack — https://adl.cs.ut.ee/lab/software
The results will 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.
During the project, you will investigate the use of vision sensing for map-based localization. The investigation includes the following steps:
1. Recognition of street names (and possibly also road direction signs) using vision
2. Matching of the perceived street-name information with streets in a given map (such as the Open Street Map)
3. Implementing a particle filter for localization within the map using the above-mentioned matches
4. Incorporation of the developed solution into the Autoware Mini software stack — https://adl.cs.ut.ee/lab/software
The results will 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.
Graduation Theses defence year
2024-2025
Supervisor
Naveed Muhammad
Spoken language (s)
English
Requirements for candidates
Level
Bachelor, Masters
Keywords
Application of contact
Name
Naveed Muhammad
Phone
E-mail