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Automatic Differentiation Techniques for Real Time Kalman Filtering
PrjCFUMax 20  +
PrjCFUMin 10  +
PrjDescription In Gauss-Newton non linear optimization on … In Gauss-Newton non linear optimization one of the most tedious part is computing Jacobians. At the AIRLab we have developed a framework for non linear 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. '''Material''' *C++ modules for Extended Kalman Filtering *libraries for automatic differentiation ( '''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:''' *C++ programming under Linux acquired:''' *C++ programming under Linux
PrjImage Image:Autodiff.png  +
PrjLevel Master of Science +
PrjResArea Robotics +
PrjResTopic None +
PrjStarts 1 January 2015  +
PrjStatus Active  +
PrjStudMax 2  +
PrjStudMin 1  +
PrjTitle Evaluation of Automatic Differentiation Techniques for Gauss-Newton based Simultaneous Localization and Mapping  +
PrjTutor User:MatteoMatteucci +
PrjType Thesis +
Categories ProjectProposal  +
Modification¬†dateThis property is a special property in this wiki. 21 December 2014 23:35:20  +
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