LED Can Backscatter: Multi-Modal Based 3D Localization via LED-Tag

Nowadays, object detection and 3D tracking have become key technologies for intelligent system or robot navigation to realize automatic obstacle avoidance and target detection, especially in low-light and night vision scenarios. In this paper, we explore the backscattering capability of LEDs and implement a multi-modal tag LED-tag to realize object detection and 3D tracking. Our basic idea is to utilize the fact that feeding modulation signals to an LED-tag can generate both RF and visual features. We fuse the depth of field information perceived from the RF domain and the pixel coordinates obtained from the visual domain to derive a 3D position by matching the decoded ID. In the RF domain, the depth of field is acquired through ultra-wideband channel measurements and estimated phase. In the visual domain, the pixel coordinate in the XOY coordinates can be extract from the image and mapped into 2D spatial coordinates. To address the limited backscatter capability of the LED-tag, we propose a multiple parallel branch model to increase backscatter paths for amplifying the LED-tag's backscattering intensity. Additionally, we propose a decoding ID scheme that utilizes a priori knowledge and repetitive samples to restore the IDs whose encoding frequency is higher than four times the sampling rate. We have implemented a prototype system and evaluated its performance in real-world environments. Extensive experimental results show that LED-tag can backscatter RF signals ranging up to 15m. Besides, the system achieves an average position error of 8cm within the range of 3m.