Optical LiDAR Communication:

As autonomous mobile robots increasingly operate in real-world environments, safety has emerged as a critical challenge, particularly regarding obstacle and pedestrian detection in building blind spots and reliable traffic signal recognition. While traditional Vehicle-to-Infrastructure (V2I) systems adopt high-capacity communication through 5G networks or via Optical Wireless Communication (OWC), these approaches require dedicated communication hardware that proves impractical for small, low-cost robots. Additionally, the communication bandwidth required for robot-oriented V2I, such as blind spot object detection and traffic signal states, is relatively limited; the high-capacity communication of 5G is often unnecessary.

To address these challenges, we propose a novel optical communication system named Optical LiDAR Communication (OLC), which repurposes existing LiDAR sensors as communication devices. By integrating LiDAR Injection with 2D Code technology, OLC achieves cost-effectiveness through V2I communication without requiring additional hardware on robots. Real-world experiments confirmed that the proposed method achieves a communication success rate of over 76% at distances up to 30 meters.

Furthermore, as a proof-of-concept, we develop two key V2I systems utilizing OLC: traffic signal information transmission and blind-spot obstacle detection, and real-time communication performance was demonstrated. These results indicate that the proposed method has potential as a V2I platform for next-generation robotics infrastructure.

Overview of OLC System

Our proposed Optical LiDAR Communication (OLC) system repurposes existing LiDAR sensors as communication receivers without additional hardware modifications. As shown in Figure.\ref{fig:olp_arch}, OLC comprises three main steps:

Transmitter Processing: Information is encoded into a 2D code like Rectangular Micro QR Code (RMQR-Code) and Data-Matrix, which is converted to a binary image that decides the laser irradiation pattern.
LiDAR Injection:}The infrastructure emits synchronized laser pulses toward the target LiDAR following the irradiation pattern. An infrared camera tracks the LiDAR's position while motors maintain targeting precision as NDSS'25 Sato et al..
Receiver Processing: The target LiDAR processes injected signals as pointcloud data, reconstructs the original 2D code, and decodes the information.

Case Studies

We propose two novel V2I systems for autonomous robots utilizing OLC: the Traffic Light Detection V2I System and the Blind Spot Detection V2I System. These systems aim to establish low-cost infrastructure communication to support safe robotic navigation.

traffic_light_for_website.mp4

Traffic Light Detection V2I System

blind_detection_for_website.mp4

Blind Spot Detection V2I System

Evaluation of OLC Robustness in Multi LiDAR Environments (supplement)

Abstraction

This experiment addresses the concern that Optical LiDAR Communication (OLC) might suffer from crosstalk or interference in environments where multiple LiDARs or optical sources are active. Because OLC relies on precise timing detection for injection, ambient LiDAR pulses could potentially be misinterpreted as legitimate signals. The goal of this experiment was to evaluate OLC’s resilience to such interference.

Experimental conditions

Setup: Two LiDARs used

Target LiDAR (receives OLC injection)
Interfering LiDAR (placed behind target)

Configuration:

Aligned in a straight line
Target LiDAR placed 1 meter from the injector
Interfering LiDAR placed 3 meters behind the target

Method: Dual-Distance OLC

Evaluation: 1000 consecutive frames

Experimental Result

Success rate: 100% (all 1000 frames successfully received)
No synchronization errors observed due to the presence of the second LiDAR
Confirms that OLC can maintain stable communication even in multi-LiDAR environments
The LiDAR Injection system employs a trigger burst transmission mechanism:

Once a synchronization signal is detected, further signals are ignored during transmission
This design prevents mis-triggering from other LiDARs or light sources

Evaluation of OLC-Induced Interference on Perception tasks (supplement)

Abstraction

This study investigates the potential interference of Optical LiDAR Communication (OLC) on LiDAR-based perception modules, specifically SLAM (Simultaneous Localization and Mapping). Since OLC involves injecting artificial point clouds, there is a risk that continuous use may degrade perception performance. To address this, the study proposes strategies to mitigate such interference and evaluates the practical impact through real and simulated experiments.

Experimental conditions

OLC Method: Multi-Distance OLC
Sensors: Real-world LiDAR + IMU dataset
SLAM method: Fast-LIO2
Environment: OLC injection simulated near a blind curve (occlusion-prone area)

Experimental Result

APE with OLC injection: 0.295 meters
Since this is below the commonly accepted 0.5 m threshold, the impact is considered negligible.

Evaluation of OLC Communication Performance with a Moving LiDAR (supplement)

Abstraction

This experiment evaluates the performance of the OLC system when the LiDAR sensor is in motion. The proposed OLC system is a wireless communication method that utilizes LiDAR sensors mounted on mobile robots. Since these robots operate while moving, it is essential for OLC to function reliably under motion. This experiment confirms the communication capability of OLC in such dynamic conditions.

Experimental conditions

OLC Method: Dual-Distance OLC

Environment: Indoor

Movement Distance: From 4 meters to 3 meters

Tested Speeds:

Multiple low-speed levels
Without high-precision IR-based tracking (as used in NDSS'25 Sato et al.)

Experimental Result

At 1.16 km/h or below:

Success Rate: 100%

At maximum tested speed (2.4 km/h):

Success Rate: Greater than 87.5%

Interpretation:

The system demonstrates reliable communication at speeds equivalent to typical service robots, confirming its suitability for real-world robotic applications.

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