Invariant EKF Design for Scan Matching-Aided Localization

Author

Barczyk, Martin ; Bonnabel, Silvere ; Deschaud, Jean-Emmanuel ; Goulette, Francois

Author_Institution

Dept. of Mech. Eng., Univ. of Alberta, Edmonton, AB, Canada

Volume

23

Issue

6

fYear

2015

Firstpage

2440

Lastpage

2448

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

cameras; image fusion; image matching; mobile robots; navigation; nonlinear filters; pose estimation; robot vision; data fusion; indoor environments; invariant EKF design; invariant extended Kalman filter; low-cost Kinect depth camera; multiplicative extended Kalman filter-based solution; onboard motion sensors; robot pose estimation; scan matching point clouds; scan matching-aided localization; Additive noise; Iterative closest point algorithm; Kalman filters; Least squares methods; Mobile robots; State estimation; Additive noise; Kalman filters; covariance matrices; iterative closest point (ICP) algorithm; least squares methods; mobile robots; state estimation; state estimation.;

fLanguage

English

Journal_Title

Control Systems Technology, IEEE Transactions on

Publisher

ieee

ISSN

1063-6536

Type

jour

DOI

10.1109/TCST.2015.2413933

Filename

7081772