Invariant EKF Design for Scan Matching-Aided Localization

Martin Barczyk; Silvère Bonnabel; Jean Emmanuel Deschaud; Francois Goulette

doi:10.1109/TCST.2015.2413933

Invariant EKF Design for Scan Matching-Aided Localization

Martin Barczyk
, Silvère Bonnabel
, Jean Emmanuel Deschaud
, Francois Goulette

University of Alberta
Mines ParisTech

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	7081772
Pages (from-to)	2440-2448
Number of pages	9
Journal	IEEE Transactions on Control Systems Technology
Volume	23
Issue number	6
DOIs	https://doi.org/10.1109/TCST.2015.2413933
Publication status	Published - 1 Nov 2015
Externally published	Yes

Keywords

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

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10.1109/TCST.2015.2413933

Cite this

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title = "Invariant EKF Design for Scan Matching-Aided Localization",

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, Kalman filters, covariance matrices, iterative closest point (ICP) algorithm, least squares methods, mobile robots, state estimation",

author = "Martin Barczyk and Silv{\`e}re Bonnabel and Deschaud, \{Jean Emmanuel\} and Francois Goulette",

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AU - Barczyk, Martin

AU - Bonnabel, Silvère

AU - Deschaud, Jean Emmanuel

AU - Goulette, Francois

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Y1 - 2015/11/1

N2 - 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.

AB - 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.

KW - Additive noise

KW - Kalman filters

KW - covariance matrices

KW - iterative closest point (ICP) algorithm

KW - least squares methods

KW - mobile robots

KW - state estimation

U2 - 10.1109/TCST.2015.2413933

DO - 10.1109/TCST.2015.2413933

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M1 - 7081772

ER -

Invariant EKF Design for Scan Matching-Aided Localization

Abstract

Keywords

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