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

TL;DR

This paper tackles real-time LiDAR point cloud compression and transmission for resource-constrained robots by proposing RCPCC, a range-image–based pipeline that uses macroblock surface fitting to remove spatial redundancy and Shape-Adaptive DCT (SA-DCT) for unfit points, all governed by a QoE-based adaptive bitrate controller. The method achieves compression rates up to 80× while preserving high downstream accuracy, and maintains real-time performance (>10 Hz) suitable for cloud offloading. Extensive experiments on KITTI and MaiCity show favorable rate–accuracy and QoE outcomes compared to Draco, PCL, and JPEG Range, with the adaptive bitrate strategy effectively stabilizing transmission under bandwidth fluctuations. The approach integrates efficient encoding, online adaptation, and practical deployment considerations, contributing a viable solution for real-time, bandwidth-robust LiDAR data transmission in robotic systems.

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.

Figures (8)

• Figure 1: A cloud service solution diagram for resource-constrained robots (left). And the downstream tasks results using the compressed point cloud (right).
• Figure 2: Overview of the proposed RCPCC framework. The input point cloud is first converted into a range image to accelerate the compression process. We use surface model fitting to eliminate spatial redundancy in the point cloud. Unfit points are transformed from the time domain to the frequency domain using SA-DCT, and the transformed results are quantized. Finally, all data required for decompression is serialized and fed into the binary entropy encoder.
• Figure 3: The range image is divided into macroblocks, with different colors representing different surfaces (left). The occupancy mask marks the location of the point cloud in the range image, and the surface block is encoded using a four-tuple (right).
• Figure 4: The F-score of mesh reconstruction and compression rate comparison of various methods.
• Figure 5: Average translation error and compression rate comparison of various methods.