EMS-SLAM: Edge-Assisted Multi-Agent System Simultaneous Localization and Mapping

Kai Hu1; Lei Zhan1; Longhao Zou2; Zuozhou Chen2; Gabriel-Miro Muntean3

10.1109/VTC2023-Spring57618.2023.10201031

2023

1090-3038

979-8-3503-1115-0

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)

2023-June

1-5

20-23 June 2023

Florence, Italy

In recent years, there has been a growing demand for robotic environment perception and autonomous driving due to the increasing popularity of visual and geometry-based localization and mapping techniques, such as simultaneous localization and mapping (SLAM). To address this trend, this paper proposes the EMS-SLAM framework, which utilizes cooperative adaptive wireless communication between servers and multi-robot agents to enhance environment perception and self-localization efficiency and accuracy. EMS-SLAM can reduce mapping time and CPU and memory utilization of individual robots while maintaining high accuracy OctoMap based on multi-map fusion and optimization. EMS-SLAM’s effectiveness and real-time performance have been validated and tested on publicly available datasets and real robots for real-world operations. The experimental results demonstrate that EMS-SLAM can reduce the CPU utilization of a single robot by approximately 10% and improve the efficiency of large-scale SLAM. The constructed OctoMap achieves centimeter-level accuracy. EMS-SLAM provides reliable, agile, and energy-efficient assistance for large-scale environment perception of robots.

cooperating multi-agent system simultaneous localization and mapping edge computing self-adaptation data fusion

第一

CPCI-S ; EI

WOS:001054797202141

20233614691426

Cooperative communication ; Data fusion ; Edge computing ; Energy efficiency ; Mapping ; Robots

Surveying:405.3 ; Energy Conservation:525.2 ; Data Communication, Equipment and Techniques:722.3 ; Digital Computers and Systems:722.4 ; Data Processing and Image Processing:723.2 ; Robotics:731.5

IEEE

1.Institue of Future Networks, Southern University of Science and Technology, Shenzhen, China
2.Department of Broadband Communication, Peng Cheng Laboratory, Shenzhen, China
3.School of Electronic Engineering, Dublin City University, Dublin, Ireland

Kai Hu,Lei Zhan,Longhao Zou,et al. EMS-SLAM: Edge-Assisted Multi-Agent System Simultaneous Localization and Mapping[C],2023:1-5.