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How to Track a Flying Object. Detection and Tracking in 3D Space. (SP, MT)
The main task of this project is to find a suitable sensor setup and implement a detection and tracking algorithm to catch flying objects with a UAV.
https://developer.dji.com/onboard-sdk/documentation/sample-doc/advanced-sensing-target-tracking.html [image]
The Mohamed Bin Zayed International Robotic Challenge (MBZIRC) is a biennial competition aiming to demonstrate the state-of-the-art in applied robotics and inspire its future. The participants need to develop complex autonomous multi-agent flying robotic systems. 2019’s first challenge requires a team of up to 3 UAVs to autonomously locate, track, and interact with a set of objects moving in space. One target is attached to a UAV, following a 3D trajectory. The other targets are balloons, tethered to bases, and randomly placed inside the arena. The targets need to be collected and delivered to a pre-specified landing location.
In this project the student will develop a detection and tracking algorithm to predict the position and velocity of a partially known object in 3D space. The main challenge is to find an algorithm that is robust to illumination changes and outliers, generalizes to a predefined set of objects, works both on long and short ranges, and outputs a high frequency accurate prediction running on a regular onboard CPU. The student will find a suitable sensor setup, summarize state of the art tracking algorithms, implement a base line solution, and test and improve the setup in simulation and on real hardware datasets.
Related work:
1. https://www.mbzirc.com/challenge/2019
2. https://github.com/ethz-asl/rotors_simulator
3. Bähnemann, Rik, et al. "The ETH-MAV Team in the MBZ International Robotics Challenge." arXiv preprint arXiv:1710.08275 (2017). [PDF] arxiv.org
4. Loianno, Giuseppe, et al. "Localization, Grasping, and Transportation of Magnetic Objects by a team of MAVs in Challenging Desert like Environments." IEEE Robotics and Automation Letters 3.3 (2018): 1576-1583. [PDF] cvut.cz
6. Li, Rui, et al. "Monocular long-term target following on UAVs." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2016. [PDF] thecvf.com
7. http://www.votchallenge.net
The Mohamed Bin Zayed International Robotic Challenge (MBZIRC) is a biennial competition aiming to demonstrate the state-of-the-art in applied robotics and inspire its future. The participants need to develop complex autonomous multi-agent flying robotic systems. 2019’s first challenge requires a team of up to 3 UAVs to autonomously locate, track, and interact with a set of objects moving in space. One target is attached to a UAV, following a 3D trajectory. The other targets are balloons, tethered to bases, and randomly placed inside the arena. The targets need to be collected and delivered to a pre-specified landing location.
In this project the student will develop a detection and tracking algorithm to predict the position and velocity of a partially known object in 3D space. The main challenge is to find an algorithm that is robust to illumination changes and outliers, generalizes to a predefined set of objects, works both on long and short ranges, and outputs a high frequency accurate prediction running on a regular onboard CPU. The student will find a suitable sensor setup, summarize state of the art tracking algorithms, implement a base line solution, and test and improve the setup in simulation and on real hardware datasets.
Related work:
1. https://www.mbzirc.com/challenge/2019
2. https://github.com/ethz-asl/rotors_simulator
3. Bähnemann, Rik, et al. "The ETH-MAV Team in the MBZ International Robotics Challenge." arXiv preprint arXiv:1710.08275 (2017). [PDF] arxiv.org
4. Loianno, Giuseppe, et al. "Localization, Grasping, and Transportation of Magnetic Objects by a team of MAVs in Challenging Desert like Environments." IEEE Robotics and Automation Letters 3.3 (2018): 1576-1583. [PDF] cvut.cz
6. Li, Rui, et al. "Monocular long-term target following on UAVs." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2016. [PDF] thecvf.com
7. http://www.votchallenge.net
- Sensor choice
- Literature review vision-based and depth-based tracking
- Development of a simulation plugin in RotorS Gazebo
- Implementation of a detection and 3D tracking algorithm
- Real world tests
- Sensor and algorithm evaluation
- Sensor choice - Literature review vision-based and depth-based tracking - Development of a simulation plugin in RotorS Gazebo - Implementation of a detection and 3D tracking algorithm - Real world tests - Sensor and algorithm evaluation
- Highly motivated and independent student
- Computer vision background (beneficial)
- C++
- ROS knowledge (optional)
- Highly motivated and independent student - Computer vision background (beneficial) - C++ - ROS knowledge (optional)
If you are interested in this project, please send your transcripts and CV to
- Margarita Grinvald margarita.grinvald@mavt.ethz.ch
- Rik Bähnemann brik@ethz.ch
If you are interested in this project, please send your transcripts and CV to - Margarita Grinvald margarita.grinvald@mavt.ethz.ch - Rik Bähnemann brik@ethz.ch