24th April 2007 — Data Fusion in the Presence of Unknown Correlations
Simon Julier will be the speaker. PLEASE NOTICE THIS WILL BE AT 3PM.
Data fusion, or multi-sensor estimation, is one of the most important capabilities of modern computing systems. However, many data fusion algorithms are based on Bayes' Rule or assumptions of conditional independence. Although these assumptions are convenient to make in theory, they are rarely satisfied in practice and can sometimes lead to catastrophic estimator failure.
In this talk I shall discuss how unknown correlations arise in two contexts: distributed data fusion and simultaneous and localisation and map building algorithms. I shall describe some of the techniques which have been developed to overcome these difficulties and, in particular, discuss the use of the Covariance Intersection (CI) algorithm for fusing state estimates when only mean and covariance information is known.
Simon J. Julier is a Senior Lecturer at the Vision, Imaging and Virtual Environments Group, in the Computer Science Department at UCL. Before joining UCL, Dr. Julier worked for nine years at the 3D Mixed and Virtual Environments Laboratory at the Naval Research Laboratory in Washington DC. There he was PI of the Battlefield Augmented Reality System (BARS), a research effort to develop man-wearable systems for providing situation awareness information. He served as the Associate Director of the 3DMVEL from 2005-2006. His research interests include user interfaces, distributed data fusion, nonlinear estimation, and simultaneous localisation and mapping.