RF-Based 3D SLAM Rivaling Vision Approaches

Abstract

This paper presents CartoRadar, a novel RF-based SLAM system that delivers high-fidelity 3D mapping with centimeter-level accuracy. CartoRadar builds on top of the advancements in learning-based RF imaging. However, learning-based systems often exhibit variation in prediction accuracy during inference. To address this challenge and enable robust RF sensing, CartoRadar introduces a novel, training-free uncertainty quantification method tailored to RF signals. Additionally, CartoRadar features an efficient SLAM algorithm that incorporates this uncertainty into the mapping process. We deploy CartoRadar on a mobile robot and conduct extensive evaluations across 14 floors in 5 buildings. Results show that CartoRadar achieves a trajectory error of 14.1 cm, outperforming camera-based baselines by 72.1%. For mapping, CartoRadar achieves an accuracy of 7.4 cm and a completion of 8.1 cm, improving over vision methods by 46.2% and 67.6%, respectively. Code, datasets, and demo videos are available on our website.