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We present a novel initialization technique for the range-aided simultaneouslocalization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurementsof point-to-point distances in addition to measurements of rigidtransformations to landmark or pose variables. Standard formulations of RA-SLAMapproach the problem as non-convex optimization, which requires a goodinitialization to obtain quality results. The initialization technique proposedhere relaxes the RA-SLAM problem to a convex problem which is then solved todetermine an initialization for the original, non-convex problem. Therelaxation is a second-order cone program (SOCP), which is derived from aquadratically constrained quadratic program (QCQP) formulation of the RA-SLAMproblem. As a SOCP, the method is highly scalable. We name this relaxationSecond-order COnic RElaxation for RA-SLAM (SCORE). To our knowledge, this workrepresents the first convex relaxation for RA-SLAM. We present real-world andsimulated experiments which show SCORE initialization permits the efficientrecovery of quality solutions for a variety of challenging single- andmulti-robot RA-SLAM problems with thousands of poses and range measurements.

SCORE: A Second-Order Conic Initialization for Range-Aided SLAM