RetroLiDAR: A Liquid-crystal Fiducial Marker System for High-fidelity Perception of Embodied AI

As embodied AI gradually transitions into practical applications, enhancing the fidelity of how embodied agents perceive the physical world has become a critical challenge. Current perception methods typically rely on computer vision-based fiducial marker systems, which suffer from limitations such as insufficient reading distance, poor localization accuracy, and high susceptibility to environmental lighting conditions. Currently, SPAD sensor-based LiDAR technology is emerging in commercial mobile devices due to its compact size, high precision, and low power consumption. This paper presents the design of the RetroLiDAR system, which chimes with the concept of backscatter in wireless technology, to create a liquid-crystal fiducial marker system that can be directly read by LiDAR. On the marker side, we use retroreflective materials to reflect the LiDAR's emitted light back and employ a liquid crystal modulator to adjust the intensity of the light signal. On the LiDAR end, we design a signal processing pipeline to demodulate the marker's modulation message using the temporal received signal strength. Experimental results from our prototype demonstrate that compared to visual fiducial markers, RetroLiDAR extends the reading distance by 2.6x compared to QR codes and by 44% compared to AprilTags, while reducing the median ranging error by 85%. We also present a low-power marker circuit design, a link budget analysis, and two proof-of-concept applications to validate the system's efficacy and practicality.