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CATEGORIES:CUED Control Group Seminars
SUMMARY:Invariant Kalman Filtering - Silvere Bonnabel\, Mi
nes Paris-Tech
DTSTART;TZID=Europe/London:20140513T140000
DTEND;TZID=Europe/London:20140513T150000
UID:TALK51770AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/51770
DESCRIPTION:For linear systems\, the well-known Kalman filter
has a well-characterized behaviour that is indepen
dent of the underlying system's trajectory in the
following sense: the covariance matrix of the esti
mate\, and the Kalman gain\, are identical for all
trajectories. On the other hand\, for non-linear
systems\, due to linearizations around the estimat
ed trajectory\, the extended Kalman filter (EKF) c
ovariance matrix\, gain\, and more generally behav
iour\, do depend on the system's trajectory\, lead
ing to possible divergence.\n\nFor non-linear syst
ems possessing symmetries\, the invariant extended
Kalman filter (IEKF) is an emerging methodology a
imed at modifying the conventional EKF so as to ac
count for those symmetries. The resulting filter's
behaviour is less (and sometimes not) dependent o
n the system's trajectory\, leading to improved st
ability and robustness properties.\n\nIn this talk
\, we will focus on aerospace and mobile robotics
applications where the conﬁguration space is a Lie
group. The IEKF design will be detailed\, along w
ith some efficient variants of it\, and some (stoc
hastic) stability properties will be discussed. Th
e results will be demonstrated experimentally on a
n attitude estimation problem for a low-cost UAV\,
and a scan matching SLAM problem for a mobile rob
ot equipped with a kinect sensor and odometers.
LOCATION:Cambridge University Engineering Department\, LR6
CONTACT:Tim Hughes
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