5Gエッジ支援ロボティクス向けL4Sによるリアルタイム点群データ伝送
Real-time Point Cloud Data Transmission via L4S for 5G-Edge-Assisted Robotics
L4SとDraco圧縮を組み合わせ、5G網上でLiDAR点群を低遅延・低損失でリアルタイム伝送し、3D SLAMのオフロード処理を実証した研究。
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著者: Gerasimos Damigos, Achilleas Santi Seisa, Nikolaos Stathoulopoulos, Sara Sandberg, George Nikolakopoulos
分類: cs.RO
原文アブストラクト
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.
