Real-time Point Cloud Data Transmission via L4S for 5G-Edge-Assisted Robotics
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911276671696896 |
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| author | Damigos, Gerasimos Seisa, Achilleas Santi Stathoulopoulos, Nikolaos Sandberg, Sara Nikolakopoulos, George |
| author_facet | Damigos, Gerasimos Seisa, Achilleas Santi Stathoulopoulos, Nikolaos Sandberg, Sara Nikolakopoulos, George |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15677 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-time Point Cloud Data Transmission via L4S for 5G-Edge-Assisted Robotics Damigos, Gerasimos Seisa, Achilleas Santi Stathoulopoulos, Nikolaos Sandberg, Sara Nikolakopoulos, George Robotics 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. |
| title | Real-time Point Cloud Data Transmission via L4S for 5G-Edge-Assisted Robotics |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.15677 |