Invariant EKF Design for Scan Matching-aided Localization

Martin Barczyk 1 Silvère Bonnabel 1 Jean-Emmanuel Deschaud 1 François Goulette 1
1 CAOR - Centre de Robotique
Abstract : Localization in indoor environments is a technique that estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an invariant extended Kalman filter (IEKF)-based and a multiplicative extended Kalman filter-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design.
Keywords : Additive noise covariance matrices iterative closest point (ICP) algorithm Kalman filters least squares methods mobile robots state estimation.
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Article dans une revue
IEEE Transactions on Control Systems Technology, Institute of Electrical and Electronics Engineers, 2015, 23 (6), pp.2440-2448. 〈10.1109/TCST.2015.2413933〉
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Dernière modification le : lundi 12 novembre 2018 - 10:56:16
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Martin Barczyk, Silvère Bonnabel, Jean-Emmanuel Deschaud, François Goulette. Invariant EKF Design for Scan Matching-aided Localization. IEEE Transactions on Control Systems Technology, Institute of Electrical and Electronics Engineers, 2015, 23 (6), pp.2440-2448. 〈10.1109/TCST.2015.2413933〉. 〈hal-01256770〉

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