Multi-Sensor Interactive Systems Group
We are part of the CRC EASE (Everyday Activity Science and Engineering).
We are member of the high-profile area Minds, Media, Machines.
Sensor data is different. Everyone knows there is noise meaning that a sensor never exactly measures the truth. However, if there was only noise, life would be simple. Instead, the incredibly complex reality creates an incredible number of surprising phenomena that affect sensor data. This is particularly relevant for computer vision where light and shadows, blur and reflections, are only a few examples.
How can a computer interpret sensor data despite these many disturbances? The answer is a combination of intuition and formalism: Judge, which effects are relevant for your application; model these effects in a probabilistic framework; conceive algorithms that find the most likely interpretation of the sensor data.
The world is moving. Hence, a particular challenge is to run a probabilistic algorithm in real-time, because probabilistic methods are often time consuming. Our main focus is on engineering implementations that are both efficient and methodically sound.
Simultaneous Localization and Mapping (SLAM), Structure from Motion (SfM), Tracking, (Extended/Unscented) Kalman Filter, Gaussian Mixture Models (GMM), Collision Avoidance, Sports Robotics