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DTSTAMP:20250725T151815Z
DESCRIPTION:Virtual Informal Systems Seminar (VISS)\n\n \n\nCentre for Inte
 lligent Machines (CIM) and Groupe d'Etudes et de Recherche en Analyse des 
 Decisions (GERAD)\n\n \n\n\n	Zoom Link\n		Meeting ID: 910 7928 6959        \n
 		Passcode: VISS\n		\n		Speaker: Silvère Bonnabel\, Professor\, University of Ne
 w Caledonia and Mines ParisTech\, France.\n		\n		Abstract:\n\n	Statistical filt
 ering is a desirable mathematical framework for the estimation of the inte
 rnal state variables of a dynamical system in the light of various sensors
 ' measurements. Since the advent of the space age\, it has played a pivota
 l role in guidance and navigation problems in aeronautics and robotics. It
 s workhorse\, the Kalman filter – albeit usable in a nonlinear context by 
 linearizing about the estimated state trajectory – deeply builds upon the 
 specificity of linear systems. As a result\, key theoretical properties ar
 e lost in nonlinear contexts\, in particular when dealing with challenging
  nonlinear problems related to the navigation of autonomous systems. Howev
 er\, it turns out that many such estimation problems bear a structure akin
  to linear systems\, after a proper embedding of the state space into a ma
 trix group has been found. Essentially\, by replacing vector addition with
  matrix multiplication\, linear observer design (or linear filter design) 
 carries over\, as well as a number of convergence and consistency guarante
 es discovered over the past fifteen years. We illustrate this perspective 
 by addressing various problems\, from the design of high performance indus
 trial inertial navigation systems to robot simultaneous localization and m
 apping. For the latter\, the geometric approach resolves problems connecte
 d to the notion of observability that have long impeded the use of the cla
 ssical extended Kalman filter. The latter results have been shown to carry
  over to applications beyond SLAM such as legged robot state estimation an
 d inertial navigation.\n\n	\n		Bio:\n\n	 \n\n	Silvère Bonnabel is Professor at 
 University of New Caledonia and Mines ParisTech\, France. He received the 
 engineering (M. Sc) degree in applied mathematics and the Ph.D. degree in 
 mathematics and control from Mines ParisTech in 2004 and 2007\, respective
 ly\, and the Habilitation in mathematics from Sorbonne University in 2014.
  He was a postdoctoral fellow at the University of Liège\, Belgium\, in 20
 08\, and was hired as a permanent faculty member at Mines ParisTech in 200
 9. In 2017\, he was an Invited Fellow of Sidney Sussex College at the Univ
 ersity of Cambridge\, UK. His primary research interests revolve around sy
 stems and control\, robotics\, with a focus on state estimation and geomet
 ric methods. He has contributed to developing products in the aeronautics 
 industry. He serves as an Associate Editor for IEEE Control Systems Magazi
 ne\, and he has also served as an Associate Editor for Systems & Control L
 etters. He is a recipient of various awards including the joint IEEE & SEE
  Glavieux Award in 2015\, 2017-2019 Automatica Paper Prize\, and European 
 Control Award 2021. \n\n	 \n\n
DTSTART:20210924T190000Z
DTEND:20210924T200000Z
LOCATION:CA\, ZOOM
SUMMARY:Geometric Filtering and Autonomous Navigation
URL:/cim/channels/event/geometric-filtering-and-autono
 mous-navigation-333478
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