Extended Kalman Filtering on Manifolds

Title:	Extended Kalman Filtering on Manifolds	Image:SE3_Manifold.jpg
Description:	Extended Kalman filtering is a well known technique for the estimation of the state of a dynamical system also used in robotics for localization and mapping. However in the basic formulation it assumes all variables to live in an Euclidean space while some components may span over the non-Euclidean 2D or 3D rotation group SO(2) or SO(3). It is thus possible to write tha Extended Kalman filter to operate on Lie Groups to take into account the presence of manifolds (http://www.ethaneade.org/latex2html/lie/lie.html). We are interestend in investigation this further applying it to the EKF-SLAM framework we have developed. Material: papers about Manifold based optimization and space representations C++ framework for EKF-SLAM Expected outcome: An extended Kalman filter which uses this new representation Required skills or skills to be acquired: Good mathematical background C++ programming under Linux
Tutor:	MatteoMatteucci (matteo.matteucci@polimi.it), SimoneCeriani (ceriani@elet.polimi.it), DavideCucci (cucci@elet.polimi.it)
Start:	2012/04/01
Students:	1 - 2
CFU:	20 - 20
Research Area:	Robotics
Research Topic:	none
Level:	Ms
Type:	Thesis
Status:	Closed