Provably Guaranteed Polytopic Uncertainty Quantification for SLAM

This paper presents provably guaranteed uncertainty quantification algorithms for 3D-3D landmark-based SLAM, using polytopes to certify uncertainty sets for mapping, pose tracking, and pose composition. It further incorporates conformal prediction to calibrate measurement uncertainty from data, balancing theoretical guarantees with practical usability.

Status: Accepted by RSS 2026.