Enhancing The Anavs Positioning System with Camera, Lidar or Radar Sensors
Vor 2 Tagen
**Enhancing the ANavS Positioning System with Camera, Lidar or Radar sensors (M/F/d)**:
Master-Thesis
- Job advertisement Id _**_MA003_**_ online since 02/2025_
The ANavS real-time positioning system provides centimeter-accurate position information and is based on a sensor fusion of satellite signals (GNSS signals) and inertial measurements from an inertial measurement unit (IMU). In addition vehicle wheel data, barometer and range measurements can be integrated to improve the performance. Camera, Lidar and radar sensors on the one hand can be used to improve and robustify the localization performance too, for example in GNSS-denied areas, such as tunnels, and on the other hand enable the detection of the environment, which includes map creation and object detection.
Another important component to improve the safety of the positioning system is integrity monitoring, which makes sure that all sensor fusion data, such as sensor raw data or navigation solutions, are valid and consistent. This component may profit from camera, Lidar and radar sensor information as well, for example using cross-checks with other sensor or solution data. Scenarios range from simple sensor data malfunctioning to complex GNSS spoofing attacks - attacks in which satellite signals are manipulated to yield a wrong position information.
**Your tasks**:
- Your topic will focus on one of the sensors camera, Lidar or radar with a focus on either object detection or localization and mapping depending on your background, interests and skills and our current priorities. Integrity monitoring is another possible thesis direction, which does not primarily focus on object detection or localization and mapping.
**Your profile**:
- Master student in computer science, robotics, or related areas
- Experience in computer vision, visual navigation, and preferably satellite-based navigation
- Very good programming skills in C++ and Python and experience in software development
- Very good analytical and debugging skills
- Experience with deep learning and machine learning techniques and frameworks, such as PyTorch or Tensorflow
- Theoretical knowledge and practical experience in Simultaneous Localization and Mapping (SLAM), or object detection methods would be ideal.
- Experience with camera, Lidar or radar sensors would be ideal.
- Strong motivation and ability to work independently
- Team player
- Good communication skills in English and preferably in German too
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