Application of extended Co variance intersection principle for mosaic-based optical positioning and navigation of underwater vehicles

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15 Scopus citations

Abstract

Mosaic-based positioning is a paradigm for the simultaneous construction of a photo-mosaic as a visual map, and its use to achieve accurate positioning [3, 9]. We discuss the application of a novel fusion principle, the so-called Extended Covariance Intersection (ECI), in addressing the mosaic-based positioning as a data fusion problem. (Extended) Kalman Filters (KF/EKF) have commonly been proposed as a viable solution for similar applications, however, this is not an optimum strategy when high correlation may exist among the estimates to be fused. Alternatively, the Covariance Intersection (CI) principle has been proposed for the fusion of highly correlated data [7]. In contrast to the EKF, the drawback is the conservative nature of the solution, as the extend of correlation becomes insignificant. The primary advantage of ECI, by decomposing the estimates from information sources into dependent and independent components, is to arrive at improved estimates, neither as over- optimistic as from an EKF, no r as over-conservative as the CI solution. Experiments with real data are presented to evaluate the performance of the proposed ECI-based formulation.

Original languageEnglish (US)
Pages (from-to)2759-2766
Number of pages8
JournalProceedings - IEEE International Conference on Robotics and Automation
Volume3
StatePublished - Jan 1 2001
Event2001 IEEE International Conference on Robotics and Automation - Seoul, Korea, Republic of
Duration: May 21 2001May 26 2001

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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