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

arXiv: https://arxiv.org/abs/2210.03177 · HTML: https://mlsanity.com/paper/2210.03177/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2022.

## Built on by (most cited first, 5 of 5)

- [Advances in Global Solvers for 3D Vision](https://mlsanity.com/paper/2602.14662/) (2026)
- [Sparse Variable Projection in Robotic Perception: Exploiting Separable Structure for Efficient Nonlinear Optimization](https://mlsanity.com/paper/2512.07969/) (2025)
- [Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization](https://mlsanity.com/paper/2401.15313/) (2024)
- [Mismatch-Robust Underwater Acoustic Localization Using A Differentiable Modular Forward Model](https://mlsanity.com/paper/2503.23260/) (2025)
- [Certifiable Factor Graph Optimization](https://mlsanity.com/paper/2603.01267/) (2026)

## Abstract (arXiv)

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.