Student Projects

Camera to GPS calibration for trains using maps

Friday, 23 Feb 2024 21:31

The ability to detect obstacles on the vehicle's path is crucial for developing autonomous vehicles, such as trains or cars. Detecting objects early enough and, therefore, at a long range is crucial, especially for heavy vehicles like trains. It is helpful to know where the train tracks and other infrastructure elements are for vision-​based systems. Rather than using machine learning approaches, which require extensive labeled training data and long inference times, one could reproject a known railway map into the camera view. Estimating the camera's exact position and rotation relative to the vehicle, and achieving a good alignment, is the challenge here.

Labels

Semester Project

Description

In this project, we aim to create a camera map overlay, which various downstream vision applications require. Typically, a GPS-​to-camera calibration uses calibration aids and a particular procedure. In the field or with existing datasets, those are typically unavailable. We aim to develop a calibration procedure based on visual cues and available map information. The challenges lie in integrating infrastructure measurement into an optimization framework and temporally consistent sensor fusion.

In this project, we aim to create a camera map overlay, which various downstream vision applications require. Typically, a GPS-​to-camera calibration uses calibration aids and a particular procedure. In the field or with existing datasets, those are typically unavailable. We aim to develop a calibration procedure based on visual cues and available map information. The challenges lie in integrating infrastructure measurement into an optimization framework and temporally consistent sensor fusion.

Work Packages

- Review of literature on extrinsic calibration. - Familiarization with existing code and work in this field. - Development of a continuous calibration and reprojection pipeline. - Testing and evaluation using recorded or simulated datasets.
  • Review of literature on extrinsic calibration.
  • Familiarization with existing code and work in this field.
  • Development of a continuous calibration and reprojection pipeline.
  • Testing and evaluation using recorded or simulated datasets.

Requirements

- Highly motivated and independent student. - Interest in SLAM and sensor fusion. - Programming skills in Python or C++. - Experience with computer vision frameworks is a plus. - Enrolled at ETH Zurich.
  • Highly motivated and independent student.
  • Interest in SLAM and sensor fusion.
  • Programming skills in Python or C++.
  • Experience with computer vision frameworks is a plus.
  • Enrolled at ETH Zurich.

Contact Details

If you are interested, please send your CV, Transcript of Records and a short paragraph explaining your motivation for the project to: cornelius.voneinem@mavt.ethz.ch, david.hug@mavt.ethz.ch, rik.baehnemann@mavt.ethz.ch

If you are interested, please send your CV, Transcript of Records and a short paragraph explaining your motivation for the project to: cornelius.voneinem@mavt.ethz.ch, david.hug@mavt.ethz.ch, rik.baehnemann@mavt.ethz.ch

More information

Open this project

Published since 2023-​02-03 , Earliest start 2023-​02-13

Organization Autonomous Systems Lab

Hosts Bähnemann Rik , von Cornelius , Hug David

Topics Information, Computing and Communication Sciences , Engineering and Technology

V4RL

The Vision for Robotics Laboratory focuses on robotic vision-based perception. The group works with small Unmanned Aerial Vehicles (UAVs) in particular, as they are some of the most challenging robotic platforms, however, our research can be applied on any robot in need of perceiving its motion and/or workspace.

Contact

LEE H 304
Vision for Robotics Lab, IRIS, D-MAVT
ETH Zurich
Leonhardstrasse 21
CH-8092 Zurich
Switzerland

Tel: +41 (0)44 632 0838
Fax: +41 (0)44 632 1181
Email: chlim (at) ethz.ch

© Copyright – V4RL 2024.
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