Cette année le voyage d’option des élèves ingénieurs de MAREVA (Automatique, robotique, vision et Morphologie mathématique) s’est déroulé à Boston. Ce voyage co-organisé par Brigitte d’Andréa-Novel, Beatri Marcotegui, Pierre Jouvelot, et Silvère Bonnabel a permis aux étudiants de visiter des laboratoires du MIT (MIT Biomimetics Lab, LIDS, Marine Robotics Group, Computer Science and Artificial Intelligence Laboratory) et des entreprises (Ecova, Intel, Datarobot, Schlumberger).
Ce fut aussi l’occasion pour Silvère Bonnabel d’intervenir dans un séminaire spécial au MIT ayant pour titre « Fixing the consistency issues of the extended Kalman filter for simultaneous localization and mapping (SLAM) ».
SLAM is a problem of robotics that has been extensively studied over the past two decades. The ability of a robot to build a map of an unknown environment and localize itself in this map is indeed the key to true autonomy. Mathematically, the problem is formalized as a non-linear Bayesian estimation/filtering problem: estimate robot’s trajectory and map given all the observations. The problem was historically tackled using the conventional extended Kalman filter (EKF), which is the state of the art for navigation and guidance. Yet, it was abandoned, owing to inconsistencies, and then replaced by particle filters and optimization based smoothing algorithms. In this talk, we will show that a simple modification to the EKF, namely a change of estimation error rooted in differential geometric motivations and Lie group theory, allows to theoretically solve those inconsistency issues. In terms of performance, the resulting novel EKF compares with state-of-the-art SLAM techniques. To conclude the talk, we will briefly discuss two points. First the application of the technique to derive a novel geometric MCSKF for visual inertial odometry (VIO or VINS). Then, the application of our geometric method to smoothing algorithms, where we show the use of an alternative geometric estimation error may alleviate the need for multiple relinearizations. Each of the two latter applications corresponds to a paper presented at IROS 2018.
Silvère Bonnabel received the engineering and Ph.D. degree in mathematics and control from Mines ParisTech, in 2004 and 2007 and held a Postdoctoral Position at University of Liège in 2008. He is currently Professor at Mines ParisTech. He was awarded the IEEE-SEE Glavieux Prize in 2015. In 2017, he was a Visiting Fellow at the University of Cambridge. He serves as an Associate Editor for Systems and Control Letters. His research includes nonlinear state estimation, optimization on manifolds, and industrial applications in the field of navigation and guidance, autonomous vehicles, and radar tracking.
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