CalTag: Robust calibration of mmWave Radar and LiDAR using backscatter tags

TL;DR

CalTag is a novel millimeter wave radar calibration method using backscatter tags, providing more reliable calibration in cluttered environments compared to traditional corner reflector techniques, thereby improving multi-sensor perception accuracy.

Contribution

Introduces CalTag, a new backscatter-based fiducial system for mmWave radars that overcomes clutter limitations of traditional corner reflector calibration methods.

Findings

CalTag outperforms corner reflectors in cluttered environments.

Calibration accuracy is significantly improved with CalTag.

Real-world tests confirm robustness and reliability of CalTag.

Abstract

The rise of automation in robotics necessitates the use of high-quality perception systems, often through the use of multiple sensors. A crucial aspect of a successfully deployed multi-sensor system is the calibration with a known object typically named fiducial. In this work, we propose a novel fiducial system for millimeter wave radars, termed as CalTag. CalTag addresses the limitations of traditional corner reflector-based calibration methods in extremely cluttered environments. CalTag leverages millimeter wave backscatter technology to achieve more reliable calibration than corner reflectors, enhancing the overall performance of multi-sensor perception systems. We compare the performance in several real-world environments and show the improvement achieved by using CalTag as the radar fiducial over a corner reflector.