Abstract: Consensus algorithms are a popular choice for computing averages and
other aggregate functions in ad-hoc wireless sensor networks. However,
existing work mostly addresses the case where the measurements lie in a
Euclidean space. In the first part of the talk we will present
Riemannian consensus, a natural extension of consensus algorithms to
Riemannian manifolds. We will discuss its convergence properties and
their dependence on various factors, such as choice of the distance
function, network connectivity, geometric configuration of the
measurements and curvature of the manifold. In the second part of the
talk we will focus on the problem of distributed 3-D camera network
localization, and show how ideas and analysis techniques from
Riemannian consensus can be extended and applied to this problem.
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