# Vision-Based Autonomous MM-Wave Reflector Using ArUco-Driven Angle-of-Arrival Estimation

arXiv: https://arxiv.org/abs/2506.05195 · HTML: https://mlsanity.com/paper/2506.05195/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [Reconfigurable Intelligent Surfaces: Principles and Opportunities](https://mlsanity.com/paper/2007.03435/) (2020)
- [Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/2102.09527/) (2021)
- [RIS-Aided Wireless Communications: Prototyping, Adaptive Beamforming, and Indoor/Outdoor Field Trials](https://mlsanity.com/paper/2103.00534/) (2021)
- [Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities](https://mlsanity.com/paper/2005.00938/) (2020)
- [Coverage Enhancement for NLOS mmWave Links Using Passive Reflectors](https://mlsanity.com/paper/1905.04794/) (2019)
- [Machine Learning on Camera Images for Fast mmWave Beamforming](https://mlsanity.com/paper/2102.07337/) (2021)
- [Computer Vision-Aided Reconfigurable Intelligent Surface-Based Beam Tracking: Prototyping and Experimental Results](https://mlsanity.com/paper/2207.05032/) (2022)
- [Camera Aided Reconfigurable Intelligent Surfaces: Computer Vision Based Fast Beam Selection](https://mlsanity.com/paper/2211.07563/) (2022)
- [LiDAR-Aided Millimeter-Wave Range Extension using a Passive Mirror Reflector](https://mlsanity.com/paper/2409.01608/) (2024)
- [Effect of Passive Reflectors for Enhancing Coverage of 28 GHz mmWave Systems in an Outdoor Setting](https://mlsanity.com/paper/1811.07400/) (2018)
- [DeepArUco++: Improved detection of square fiducial markers in challenging lighting conditions](https://mlsanity.com/paper/2411.05552/) (2024)

## Abstract (arXiv)

Reliable millimeter-wave (mmWave) communication in non-line-of-sight (NLoS) conditions remains a major challenge for both military and civilian operations, especially in urban or infrastructure-limited environments. This paper presents a vision-aided autonomous reflector system designed to enhance mmWave link performance by dynamically steering signal reflections using a motorized metallic plate. The proposed system leverages a monocular camera to detect ArUco markers on allied transmitter and receiver nodes, estimate their angles of arrival, and align the reflector in real time for optimal signal redirection. This approach enables selective beam coverage by serving only authenticated targets with visible markers and reduces the risk of unintended signal exposure. The designed prototype, built on a Raspberry Pi 4 and low-power hardware, operates autonomously without reliance on external infrastructure or GPS. Experimental results at 60,GHz demonstrate a 23,dB average gain in received signal strength and an 0.89 probability of maintaining signal reception above a target threshold of -65 dB in an indoor environment, far exceeding the static and no-reflector baselines. These results demonstrate the system's potential for resilient and adaptive mmWave connectivity in complex and dynamic environments.