Hybrid Least Squares for Collaborative Localization: Comparative Analysis and Integrated Outlier Rejection

Ali Khalajmehrabadi, Nikolaos Gatsis, Daniel Pack, and David Akopian

Peer Reviewed

Abstract:	This paper addresses the problem of anchor-free multi-agent collaborative localization. We discuss three different coarse localization schemes: 1) Intuitive Coarse Localization (ICL), 2) Multidimensional Scaling (MDS) and 3) Semidefinite Programming (SDP). Then, a unified set of sequential and parallel LS techniques are applied to modify these coarse estimates. An outlier detection procedure is also introduced for localization with the presence of outliers. The numerical comparisons yield important insights to practitioners.
Published in:	Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020) September 21 - 25, 2020
Pages:	2468 - 2474
Cite this article:	Khalajmehrabadi, Ali, Gatsis, Nikolaos, Pack, Daniel, Akopian, David, "Hybrid Least Squares for Collaborative Localization: Comparative Analysis and Integrated Outlier Rejection," Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020), September 2020, pp. 2468-2474. https://doi.org/10.33012/2020.17696
Full Paper:	ION Members/Non-Members: 1 Download Credit Sign In