ICRA 2012 Paper Abstract

Close

Paper TuB09.2

Zhou, Ke (University of Minnesota), Roumeliotis, Stergios (University of Minnesota)

A Sparsity-aware QR Decomposition Algorithm for Efficient Cooperative Localization

Scheduled for presentation during the Regular Session "Localization" (TuB09), Tuesday, May 15, 2012, 10:45−11:00, Meeting Room 9 (Sa)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 12, 2017

Keywords Localization, Sensor Fusion, Sensor Networks

Abstract

This paper focuses on reducing the computational complexity of the extended Kalman filter (EKF)-based multi-robot cooperative localization (CL) by taking advantage of the sparse structure of the measurement Jacobian matrix H. In contrast to the standard EKF update, whose complexity is up to O(N4) (N is the number of robots in a team), we introduce a Modified Householder QR algorithm which fully exploits the sparse structure of the matrix H, and prove that the overall complexity of the EKF update, based on our QR factorization scheme, reduces to O(N3). Finally, we validate the Modified Householder QR algorithm through extensive simulations, and demonstrate its superior performance both in terms of accuracy and CPU runtime, as compared to the current state-of-the-art QR decomposition algorithm for sparse matrices.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2017 PaperCept, Inc.
Page generated 2017-12-12  17:38:10 PST  Terms of use