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

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.

Figures (9)

• Figure 1: The upper plot shows the first harmonics. The lower plot shows the Range-Doppler FFT with modulated backscatter signals. High-intensity peaks inside red boxes are the modulated frequencies corresponding to harmonics at positive and negative frequency axes.
• Figure 2: Flowchart for tag detection
• Figure 3: CalTag mounting structure
• Figure 4: Comparison of three clutter levels. The top images show the environments, in which regions within the red polygons are clutter sources. The orange box marks the position of the CalTag. Bottom radar plots show the clutter sources and other background noise in polar coordinates. The red fan-shaped boundary marks the data collection region. The blue arrow maps the position of the clutter source from the image to the radar plot.
• Figure 5: Comparison of the calibration results between using corner reflector and using CalTag in an environment with and without strong clutters. Red points are radar point clouds. Blue points are LiDAR point clouds.