Automatic Differentiation Techniques for Real Time Kalman Filtering
|Title:||Evaluation of Automatic Differentiation Techniques for Real Time Kalman Filtering|| |
|Description:||When writing an Extended Kalman filter one of the most tedious part is computing Jacobians. At the AIRLab we have developed a framework for EKF based Simultaneous Localization and Mapping suitable for different motion models and measurement equations, but any time you need to change something you need to recompute the required Jacobian. Automatic differentiation is a tool for the automatic differentiation of source code either at compiling time or at runtime; we are interested in testing these techniques in the software we have developed and compare their performance with respect to (cumbersome) optimized computation.
Expected outcome: New modules implementations based on automatic differentiation A comparison between the old stuff and new approach
Required skills or skills to be acquired:
|Tutor:||MatteoMatteucci (firstname.lastname@example.org), SimoneCeriani (email@example.com), DavideCucci (firstname.lastname@example.org)|
|Students:||1 - 2|
|CFU:||10 - 20|