Optical LiDAR Communication:
Repurposing Existing LiDAR Sensors
for Infrastructure-to-Vehicle Communication
(supplement)
Embedded Files
Evaluation of OLC Robustness in Multi LiDAR Environments
Evaluation of OLC Robustness in Multi LiDAR Environments
Abstraction
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
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
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
Evaluation of OLC-Induced Interference on Perception tasks
Abstraction
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
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
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 Permormance with a Moving LiDAR
Evaluation of OLC Communication Permormance with a Moving LiDAR
Abstraction
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
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
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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