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

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.

Mots clés: Additive noise; covariance matrices; iterative closest point (ICP) algorithm; Kalman filters; least squares methods; mobile robots; state estimation.

Domaines: Informatique [cs]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Informatique [cs]; Automatique

Martin Barczyk, Silvère Bonnabel, Jean-Emmanuel Deschaud, François Goulette. Invariant EKF Design for Scan Matching-aided Localization. IEEE Transactions on Control Systems Technology, 2015, 23 (6), pp.2440-2448. ⟨10.1109/TCST.2015.2413933⟩. ⟨hal-01256770⟩