  Modern state estimation is often formulated as an optimization problem and
solved using efficient local search methods. These methods at best guarantee
convergence to local minima, but, in some cases, global optimality can also be
certified. Although such global optimality certificates have been well
established for 3D \textit{pose-graph optimization}, the details have yet to be
worked out for the 3D landmark-based SLAM problem, in which estimated states
include both robot poses and map landmarks. In this paper, we address this gap
by using a graph-theoretic approach to cast the subproblems of landmark-based
SLAM into a form that yields a sufficient condition for global optimality.
Efficient methods of computing the optimality certificates for these
subproblems exist, but first require the construction of a large data matrix.
We show that this matrix can be constructed with complexity that remains linear
in the number of landmarks and does not exceed the state-of-the-art
computational complexity of one local solver iteration. We demonstrate the
efficacy of the certificate on simulated and real-world landmark-based SLAM
problems. We also integrate our method into the state-of-the-art SE-Sync
pipeline to efficiently solve landmark-based SLAM problems to global
optimality. Finally, we study the robustness of the global optimality
certificate to measurement noise, taking into consideration the effect of the
underlying measurement graph.

Robotics