| Type |
SEP, IDP, BA, DA, MA |
| Supervisor |
Prof. Dr.-Ing. Darius Burschka |
| Advisor |
Elmar Mair |
| Research Area |
Sensor Fusion, Visual Navigation, Embedded Systems |
| Associated Project |
DLR 3dMo |
| Programming Language |
C, C++ |
| Required Skills |
|
| Useful Knowledge |
Kalman Filters |
| Language |
English or German |
Description
Do you like modeling? Do you like to work with sensors of different domains?
| Visual Navigation has become an engaging field in the last years, as cameras are not only noninvasive and need low-current, but they became more and more compact, accurate and cheaper. However, the high data rate of cameras seems to be the bottleneck for embedded systems, which are also often highly dynamic. Thus, the fusion of a camera system and an inertial measurement unit seems to cover those lack and allow robust tracking also at low frame rates. |
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The fusion of IMU data and camera data turned out to be quite tricky and the right parametrization for Kalman filtering is difficult to find. Thus, the sensor data and its uncertainty has to be evaluated in various tests and different settings to find similarities and rules for a proper parametrization. A first application of the results would be on the DLR 3D modeler and flying robots. |
For more information to this scientific highly interesting work, please contact
Elmar Mair.
Literature
