  We consider the problem of estimating the locations of a set of points in a
k-dimensional euclidean space given a subset of the pairwise distance
measurements between the points. We focus on the case when some fraction of
these measurements can be arbitrarily corrupted by large additive noise. Given
that the problem is highly non-convex, we propose a simple semidefinite
programming relaxation that can be efficiently solved using standard
algorithms. We define a notion of non-contractibility and show that the
relaxation gives the exact point locations when the underlying graph is
non-contractible. The performance of the algorithm is evaluated on an
experimental data set obtained from a network of 44 nodes in an indoor
environment and is shown to be robust to non-line-of-sight errors.
