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Construction robotics
Many tasks in the construction industry are repetitive and can be dangerous for human workers. We plan to develop robotic systems that can safely navigate and collaborate in these environments.
Keywords: Sensor Fusion, Sensors, State Estimation, SLAM, Localization, Place recognition, Deep Learning, Machine Learning, Robot Perception
Many tasks in the construction industry are repetitive and can be dangerous for human workers. Furthermore, new health regulations on construction sites render many classic techniques infeasible. Yet, construction industry is one of the least digitalized industries. We plan to enable robots to safely navigate and collaborate in these environments. However, construction tasks require very high precision which today's mobile robots cannot achieve. In our focus group on Construction Robotics, we perform research on novel techniques for robot perception in challenging environments. These entail SLAM, Semantic scene understanding (e.g., Learning-based), high-accuracy operation, sensor fusion and calibration. We continuously offer student projects in these areas.
Many tasks in the construction industry are repetitive and can be dangerous for human workers. Furthermore, new health regulations on construction sites render many classic techniques infeasible. Yet, construction industry is one of the least digitalized industries. We plan to enable robots to safely navigate and collaborate in these environments. However, construction tasks require very high precision which today's mobile robots cannot achieve. In our focus group on Construction Robotics, we perform research on novel techniques for robot perception in challenging environments. These entail SLAM, Semantic scene understanding (e.g., Learning-based), high-accuracy operation, sensor fusion and calibration. We continuously offer student projects in these areas.
- Familiarize yourself with the current state of the art.
- Investigate existing solutions to the specific needs of construction robots.
- Develop novel hardware & algorithms to address the challenges of construction sites.
- Test your algorithms on evaluation data, and real robots.
- Familiarize yourself with the current state of the art. - Investigate existing solutions to the specific needs of construction robots. - Develop novel hardware & algorithms to address the challenges of construction sites. - Test your algorithms on evaluation data, and real robots.
- Good understanding of algorithmic challenges.
- Knowledge of C++ is mandatory, Python is recommended.
- Knowledge of ROS is recommended.
- Knowledge in two of the following areas: SLAM, Localization, sensor fusion, computer vision, deep learning, hardware design.
- Be curious about pushing the limits of today's robotics.
- Strong self-motivation and critical mind.
- Students from outside of D-MAVT (particularly, from D-INFK, D-ITET, D-PHYS, and D-MATH) are also highly encouraged to apply.
- Good understanding of algorithmic challenges. - Knowledge of C++ is mandatory, Python is recommended. - Knowledge of ROS is recommended. - Knowledge in two of the following areas: SLAM, Localization, sensor fusion, computer vision, deep learning, hardware design. - Be curious about pushing the limits of today's robotics. - Strong self-motivation and critical mind. - Students from outside of D-MAVT (particularly, from D-INFK, D-ITET, D-PHYS, and D-MATH) are also highly encouraged to apply.
If you are interested, please send your transcripts and CV to Abel Gawel (gawela@ethz.ch) and Hermann Blum (blumh@ethz.ch). Please also indicate your preferred field of research.
If you are interested, please send your transcripts and CV to Abel Gawel (gawela@ethz.ch) and Hermann Blum (blumh@ethz.ch). Please also indicate your preferred field of research.