Real-time Point Cloud Data Transmission via L4S for 5G-Edge-Assisted Robotics

TL;DR

This paper introduces a real-time LiDAR data transmission framework for 5G networks that combines adaptive rate control, advanced encoding, and compression to enable low-latency, high-accuracy robotic applications over urban environments.

Contribution

It extends L4S-enabled transmission with Draco compression for dynamic, high-bitrate 3D LiDAR data, ensuring minimal delay and error in real-world 5G scenarios.

Findings

Effective real-time LiDAR streaming over 5G in urban environments.

Maintains low latency and loss while supporting robotic data processing.

Validates framework with real-world 3D SLAM evaluations.

Abstract

This article presents a novel framework for real-time Light Detection and Ranging (LiDAR) data transmission that leverages rate-adaptive technologies and point cloud encoding methods to ensure low-latency, and low-loss data streaming. The proposed framework is intended for, but not limited to, robotic applications that require real-time data transmission over the internet for offloaded processing. Specifically, the Low Latency, Low Loss, Scalable Throughput L4S-enabled SCReAM v2 transmission framework is extended to incorporate the Draco geometry compression algorithm, enabling dynamic compression of high-bitrate 3D LiDAR data according to the sensed channel capacity and network load. The low-latency 3D LiDAR streaming system is designed to maintain minimal end-to-end delay while constraining encoding errors to meet the accuracy requirements of robotic applications. We demonstrate the effectiveness of the proposed method through real-world experiments conducted over a public 5G network across multi-kilometer urban environments. The low-latency and low-loss requirements are preserved, while real-time offloading and evaluation of 3D SLAM algorithms are used to validate the framework's performance in practical use cases.