Map-based localization in autonomous driving using lidar
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. One robust technique for vehicles to localize is using particle filter [1, 2, 3], given a map of the environment. Recently, deep-learning approaches to map-based localization have also been proposed in the literature [4, 5].
During the project, you will investigate lidar sensors for map-based localization. You can start by experimenting with lidar data acquired using the ADL Lexus vehicle and then move to real-time implementation on-board the vehicle (within our Autoware Mini software stack - https://adl.cs.ut.ee/lab/software). The investigation includes aspects such as:
- What map modality to use
- What is the lower bound on how rich the lidar sensor data should be, in order to perform localization. The vehicle is equipped with multi-beam lidars from Velodyne and Ouster. So the natural question is if we need all the laser beams? Do we need 16, 8, or is even a single beam enough?
- Incorporation of your proposed solution into Autoware Mini.
The results will also potentially be published in the form of a research paper.
Some relevant literature:
[1] Jesse Levinson and Sebastian 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.
[4] Xinkai Wei, Ioan Andrei Bârsan, Shenlong Wang, Julieta Martinez, Raquel Urtasun, "Learning to Localize Through Compressed Binary Maps", Accessible at: https://arxiv.org/abs/2012.10942
[5] Ioan Andrei Bârsan, Shenlong Wang, Andrei Pokrovsky, Raquel Urtasun, "Learning to Localize Using a LiDAR Intensity Map", Accessible at: https://arxiv.org/abs/2012.10902
During the project, you will investigate lidar sensors for map-based localization. You can start by experimenting with lidar data acquired using the ADL Lexus vehicle and then move to real-time implementation on-board the vehicle (within our Autoware Mini software stack - https://adl.cs.ut.ee/lab/software). The investigation includes aspects such as:
- What map modality to use
- What is the lower bound on how rich the lidar sensor data should be, in order to perform localization. The vehicle is equipped with multi-beam lidars from Velodyne and Ouster. So the natural question is if we need all the laser beams? Do we need 16, 8, or is even a single beam enough?
- Incorporation of your proposed solution into Autoware Mini.
The results will also potentially be published in the form of a research paper.
Some relevant literature:
[1] Jesse Levinson and Sebastian 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.
[4] Xinkai Wei, Ioan Andrei Bârsan, Shenlong Wang, Julieta Martinez, Raquel Urtasun, "Learning to Localize Through Compressed Binary Maps", Accessible at: https://arxiv.org/abs/2012.10942
[5] Ioan Andrei Bârsan, Shenlong Wang, Andrei Pokrovsky, Raquel Urtasun, "Learning to Localize Using a LiDAR Intensity Map", Accessible at: https://arxiv.org/abs/2012.10902
Lõputöö kaitsmise aasta
2023-2024
Juhendaja
Naveed Muhammad
Suhtlemiskeel(ed)
inglise keel
Nõuded kandideerijale
Tase
Bakalaureus, Magister
Märksõnad
Kandideerimise kontakt
Nimi
Naveed Muhammad
Tel
E-mail