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

Gerasimos Damigos, Achilleas Santi Seisa, Nikolaos Stathoulopoulos, Sara Sandberg, George Nikolakopoulos

TL;DR

This work tackles real-time uplink transmission of 3D LiDAR data for edge-assisted robotics over wireless networks by extending L4S-enabled SCReAM v2 with LiDAR-aware encoding. It couples a Draco-based point-cloud encoder to a rate-adaptive transmission framework via a Compression Parameter Predictor and a Residual Error Optimizer, linking target bitrate to encoder settings while enforcing a maximum allowable distortion $\varepsilon$. The approach is validated through field experiments on public 5G networks, showing the ability to maintain low end-to-end latency and low loss while supporting real-time SLAM offloading and robust operation under varying radio conditions. The paper also provides an open-source implementation to facilitate reproducibility and further research in edge-enabled robotics.

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.

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

TL;DR

This work tackles real-time uplink transmission of 3D LiDAR data for edge-assisted robotics over wireless networks by extending L4S-enabled SCReAM v2 with LiDAR-aware encoding. It couples a Draco-based point-cloud encoder to a rate-adaptive transmission framework via a Compression Parameter Predictor and a Residual Error Optimizer, linking target bitrate to encoder settings while enforcing a maximum allowable distortion . The approach is validated through field experiments on public 5G networks, showing the ability to maintain low end-to-end latency and low loss while supporting real-time SLAM offloading and robust operation under varying radio conditions. The paper also provides an open-source implementation to facilitate reproducibility and further research in edge-enabled robotics.

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.

Paper Structure

This paper contains 15 sections, 13 equations, 7 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminaries
  4. L4S-enabled SCReAM v2 in a nutshell
  5. Problem Formulation
  6. 1) Data encoding at the required bitrate:
  7. 2) Confining Compression Residual:
  8. 3) Minimum $\varepsilon$ per considered application:
  9. The Proposed System Architecture
  10. Compression Parameter Predictor
  11. Residual Error Optimizer
  12. Experimental Validation
  13. Framework evaluation
  14. Use-case evaluation
  15. Conclusions and Future Developments

Figures (7)

  • Figure 1: Sender transmission pipeline. Gray colored boxes depict the underline framework components, while the rest depict expansions native to the proposed methodology. The receiving side (not shown here) is responsible for rendering the 3D LiDAR data and generating the RTCP feedback.
  • Figure 2: Left: mean point-to-point error between raw LiDAR scans and their Draco-compressed counterparts as a function of quantization bits $q$. Right: Absolute Trajectory Error (ATE, RMSE) of KISS-SLAM versus $q$ for the same sweep. The ground-truth trajectory is obtained by running KISS-SLAM on the uncompressed data, while the compressed runs are evaluated against this reference.
  • Figure 3: Reference window $w_{ref}$ vs. Bytes in Flight $b_f$.
  • Figure 4: Smooth round trip time $\hat{\tau}_{RTT}$ vs. estimated queue delay.
  • Figure 5: Framework throughput vs. target bitrate $r_\text{trg}$, vs. predicted encoder bitrate.
  • ...and 2 more figures