  Pose graph optimization is a non-convex optimization problem encountered in
many areas of robotics perception. Its convergence to an accurate solution is
conditioned by two factors: the non-linearity of the cost function in use and
the initial configuration of the pose variables. In this paper, we present
HiPE, a novel hierarchical algorithm for pose graph initialization. Our
approach exploits a coarse-grained graph that encodes an abstract
representation of the problem geometry. We construct this graph by combining
maximum likelihood estimates coming from local regions of the input. By
leveraging the sparsity of this representation, we can initialize the pose
graph in a non-linear fashion, without computational overhead compared to
existing methods. The resulting initial guess can effectively bootstrap the
fine-grained optimization that is used to obtain the final solution. In
addition, we perform an empirical analysis on the impact of different cost
functions on the final estimate. Our experimental evaluation shows that the
usage of HiPE leads to a more efficient and robust optimization process,
comparing favorably with state-of-the-art methods.
