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

2025-11-11

AI Summary

To address high latency and packet loss in LiDAR point cloud streaming for 5G edge-coordinated robotics, this paper proposes an end-edge collaborative real-time point cloud streaming method. The core innovation lies in the first integration of the L4S-enhanced SCReAM v2 congestion control protocol with Draco geometric compression, enabling dynamic adjustment of point cloud compression ratios based on real-time channel conditions—thereby optimizing bandwidth adaptation while preserving reconstruction fidelity. The system integrates 5G edge computing with a real-time SLAM validation framework. Field evaluations over multi-kilometer urban public 5G networks achieve end-to-end latency in the millisecond range and packet loss below 0.1%, significantly enabling remote real-time mapping and perception. This work establishes a deployable end-edge co-design paradigm for reliable, low-latency transmission of 3D perception data under highly dynamic network conditions.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Transmitting real-time LiDAR data with low latency and minimal loss
• Compressing high-bitrate 3D point clouds for dynamic network conditions
• Enabling robotic applications requiring real-time offloaded processing capabilities

Innovation

• L4S-enabled SCReAM v2 for low-latency transmission
• Draco compression adapts to network conditions
• Real-time 3D LiDAR streaming with minimal delay