TY - GEN
T1 - Real-Time LiDAR Point Cloud Compression and Transmission for Resource-Constrained Robots
AU - Cao, Yuhao
AU - Wang, Yu
AU - Chen, Haoyao
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 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×to 80× while maintaining high accuracy for downstream applications. our method significantly outperforms other baselines in terms of accuracy when the compression rate exceeds 70×. 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.
AB - 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×to 80× while maintaining high accuracy for downstream applications. our method significantly outperforms other baselines in terms of accuracy when the compression rate exceeds 70×. 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.
UR - https://www.scopus.com/pages/publications/105016637311
U2 - 10.1109/ICRA55743.2025.11127859
DO - 10.1109/ICRA55743.2025.11127859
M3 - 会议稿件
AN - SCOPUS:105016637311
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 12789
EP - 12795
BT - 2025 IEEE International Conference on Robotics and Automation, ICRA 2025
A2 - Ott, Christian
A2 - Admoni, Henny
A2 - Behnke, Sven
A2 - Bogdan, Stjepan
A2 - Bolopion, Aude
A2 - Choi, Youngjin
A2 - Ficuciello, Fanny
A2 - Gans, Nicholas
A2 - Gosselin, Clement
A2 - Harada, Kensuke
A2 - Kayacan, Erdal
A2 - Kim, H. Jin
A2 - Leutenegger, Stefan
A2 - Liu, Zhe
A2 - Maiolino, Perla
A2 - Marques, Lino
A2 - Matsubara, Takamitsu
A2 - Mavromatti, Anastasia
A2 - Minor, Mark
A2 - O'Kane, Jason
A2 - Park, Hae Won
A2 - Park, Hae-Won
A2 - Rekleitis, Ioannis
A2 - Renda, Federico
A2 - Ricci, Elisa
A2 - Riek, Laurel D.
A2 - Sabattini, Lorenzo
A2 - Shen, Shaojie
A2 - Sun, Yu
A2 - Wieber, Pierre-Brice
A2 - Yamane, Katsu
A2 - Yu, Jingjin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE International Conference on Robotics and Automation, ICRA 2025
Y2 - 19 May 2025 through 23 May 2025
ER -