  We present a novel initialization technique for the range-aided simultaneous
localization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurements
of point-to-point distances in addition to measurements of rigid
transformations to landmark or pose variables. Standard formulations of RA-SLAM
approach the problem as non-convex optimization, which requires a good
initialization to obtain quality results. The initialization technique proposed
here relaxes the RA-SLAM problem to a convex problem which is then solved to
determine an initialization for the original, non-convex problem. The
relaxation is a second-order cone program (SOCP), which is derived from a
quadratically constrained quadratic program (QCQP) formulation of the RA-SLAM
problem. As a SOCP, the method is highly scalable. We name this relaxation
Second-order COnic RElaxation for RA-SLAM (SCORE). To our knowledge, this work
represents the first convex relaxation for RA-SLAM. We present real-world and
simulated experiments which show SCORE initialization permits the efficient
recovery of quality solutions for a variety of challenging single- and
multi-robot RA-SLAM problems with thousands of poses and range measurements.
