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

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.