Real-Time LiDAR Point Cloud Compression and Transmission for Resource-constrained Robots

TL;DR

This paper introduces RCPCC, a novel framework for real-time LiDAR point cloud compression and transmission tailored for resource-limited robots, achieving high compression rates while maintaining accuracy and adaptive QoE.

Contribution

The paper presents a new compression and transmission method that combines surface fitting, SA-DCT transformation, and adaptive bitrate control for resource-constrained robotic applications.

Findings

Achieves 40× to 80× compression rates with high accuracy.

Outperforms baselines when compression exceeds 70×.

Significantly improves QoE under limited bandwidth conditions.

Abstract

LiDARs are widely used in autonomous robots due to their ability to provide accurate environment structural information. However, the large size of point clouds poses challenges in terms of data storage and transmission. In this paper, we propose a novel point cloud compression and transmission framework for resource-constrained robotic applications, called RCPCC. We iteratively fit the surface of point clouds with a similar range value and eliminate redundancy through their spatial relationships. Then, we use Shape-adaptive DCT (SA-DCT) to transform the unfit points and reduce the data volume by quantizing the transformed coefficients. We design an adaptive bitrate control strategy based on QoE as the optimization goal to control the quality of the transmitted point cloud. Experiments show that our framework achieves compression rates of 40$\times$ to 80$\times$ while maintaining high accuracy for downstream applications. our method significantly outperforms other baselines in terms of accuracy when the compression rate exceeds 70$\times$. Furthermore, in situations of reduced communication bandwidth, our adaptive bitrate control strategy demonstrates significant QoE improvements. The code will be available at https://github.com/HITSZ-NRSL/RCPCC.git.