  Pose Graph Optimization (PGO) is an important non-convex optimization problem
and is the state-of-the-art formulation for SLAM in robotics. It also has
applications like camera motion estimation, structure from motion and 3D
reconstruction in machine vision. Recent researches have shown the importance
of good initialization to bootstrap well-known iterative PGO solvers to
converge to good solutions. The state-of-the-art initialization methods,
however, works in low noise or eventually moderate noise problems, and they
fail in challenging problems with high measurement noise. Consequently,
iterative methods may get entangled in local minima in high noise scenarios. In
this paper we present an initialization method which uses orientation
measurements and then present a convergence analysis of our iterative
algorithm. We show how the algorithm converges to global optima in noise-free
cases and also obtain a bound for the difference between our result and the
optimum solution in scenarios with noisy measurements. We then present our
second algorithm that uses both relative orientation and position measurements
to obtain a more accurate least squares approximation of the problem that is
again solved iteratively. In the convergence proof, a structural coefficient
arises that has important influence on the basin of convergence. Interestingly,
simulation results show that this coefficient also affects the performance of
other solvers and so it can indicate the complexity of the problem.
Experimental results show the excellent performance of the proposed
initialization algorithm, specially in high noise scenarios.
