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High-Speed SLAM for Rail Vehicles
The goal of this work is to develop a high-speed SLAM system that is suitable for usage on rail vehicles (e.g. trains). For this we aim to use modern sensors (DVS) as well as domain specific information, such as available track maps.
Localization for railway vehicles, such as trains and trams, is crucial to enable a safe and efficient usage of the infrastructure. Current safety systems are based on infrastructure-side beacons, so called balises, which only provide very low resolution positioning information. To increase the systems efficiency and still ensure safe operation a continuous on-board localization would be of significant advantage.
Several parts of such a SLAM system for the railway environment already exist. In this project we aim at integrating these modules into a whole pipeline to enable continuous onboard localization. An event-based mapping system can be combined with available railway track maps and a constellation based localization scheme to provide accurate and continuous information of the vehicle’s position.
Localization for railway vehicles, such as trains and trams, is crucial to enable a safe and efficient usage of the infrastructure. Current safety systems are based on infrastructure-side beacons, so called balises, which only provide very low resolution positioning information. To increase the systems efficiency and still ensure safe operation a continuous on-board localization would be of significant advantage.
Several parts of such a SLAM system for the railway environment already exist. In this project we aim at integrating these modules into a whole pipeline to enable continuous onboard localization. An event-based mapping system can be combined with available railway track maps and a constellation based localization scheme to provide accurate and continuous information of the vehicle’s position.
- Review of literature.
- Getting familiar with existing codebases.
- Design of full SLAM pipeline.
- Map structure.
- Development of the full localization system.
- Evaluation based on recorded datasets.
- Review of literature. - Getting familiar with existing codebases. - Design of full SLAM pipeline. - Map structure. - Development of the full localization system. - Evaluation based on recorded datasets.
- Highly motivated and independent student.
- Interest in Computer Vision and Robotics.
- Programming skills in Python or C++.
- Previous knowledge in ROS.
- Enrolled at ETH Zurich.
- Highly motivated and independent student. - Interest in Computer Vision and Robotics. - Programming skills in Python or C++. - Previous knowledge in ROS. - Enrolled at ETH Zurich.
If you are interested, please contact Andrei Cramariuc (andrei.cramariuc@mavt.ethz.ch) and Cornelius von Einem (cornelius.voneinem@mavt.ethz.ch), sending your CV, transcript of records and a short paragraph on why you want to work on this project.
If you are interested, please contact Andrei Cramariuc (andrei.cramariuc@mavt.ethz.ch) and Cornelius von Einem (cornelius.voneinem@mavt.ethz.ch), sending your CV, transcript of records and a short paragraph on why you want to work on this project.