面向大规模地下空间的多智能体端边协作全局SLAM方法

张奎元; 张启亮; 陈朋朋; 高守婉

doi:10.12263/DZXB.20250472

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面向大规模地下空间的多智能体端边协作全局SLAM方法

大模型与互联网 | 更新时间：2026-02-10

- 面向大规模地下空间的多智能体端边协作全局SLAM方法
- Robots-Edge Collaborative Absolute SLAM in Large-Scale Underground Environments
- 电子学报 2025年53卷第11期页码：3852-3864
- 作者机构：
  
  1.中国矿业大学计算机科学与技术学院/人工智能学院，江苏徐州 221116
  2.中国矿业大学深圳研究院，广东深圳 518057
  3.矿山数字化教育部工程研究中心，江苏徐州 221116
- 作者简介：
  
  [ "张奎元男，1997年1月出生于江苏省徐州市.现为中国矿业大学计算机科学与技术学院/人工智能学院博士研究生.主要研究方向为移动边缘计算与智能无线感知.E-mail: zhangky@cumt.edu.cn" ]
  [ "张启亮男，1979年11月出生于山东省菏泽市.现为中国矿业大学计算机科学与技术学院/人工智能学院兼职教授.主要研究方向为工业互联网、计算机视觉与智慧矿山.E-mail: zql@xcmg.com" ]
  [ "陈朋朋男，1983年11月出生于山东省济宁市.现为中国矿业大学计算机科学与技术学院/人工智能学院教授、博士生导师.主要研究方向为物联网与大数据建模.E-mail: henp@cumt.edu.cn" ]
  [ "高守婉女，1982年10月出生于山东省烟台市.现为中国矿业大学计算机科学与技术学院/人工智能学院副教授、硕士生导师.主要研究方向为信号估计与群智感知.E-mail: gaoshouwan@cumt.edu.cn" ]
- 基金信息：
  
  国家重点研发计划(2024YFC3015701);国家自然科学基金(62272462);江苏省杰出青年基金(BK20230045);深圳市科技计划(JCYJ20230807154300002)
- DOI：10.12263/DZXB.20250472
  中图分类号： TP393;
- 收稿：2025-06-04，
  
  录用：2025-11-11，
  
  纸质出版：2025-11-25
- 稿件说明：
移动端阅览
张奎元, 张启亮, 陈朋朋, 等. 面向大规模地下空间的多智能体端边协作全局SLAM方法[J]. 电子学报, 2025, 53(11): 3852-3864.

ZHANG Kui-yuan, ZHANG Qi-liang, CHEN Peng-peng, et al. Robots-Edge Collaborative Absolute SLAM in Large-Scale Underground Environments[J]. Acta Electronica Sinica, 2025, 53(11): 3852-3864.
张奎元, 张启亮, 陈朋朋, 等. 面向大规模地下空间的多智能体端边协作全局SLAM方法[J]. 电子学报, 2025, 53(11): 3852-3864. DOI：10.12263/DZXB.20250472

ZHANG Kui-yuan, ZHANG Qi-liang, CHEN Peng-peng, et al. Robots-Edge Collaborative Absolute SLAM in Large-Scale Underground Environments[J]. Acta Electronica Sinica, 2025, 53(11): 3852-3864. DOI：10.12263/DZXB.20250472

摘要

随着地下空间开发不断朝着深部化、大型化与无人化方向发展，移动机器人在地下探测及救援等方面发挥着重要作用.即时定位与建图（Simultaneous Localization And Mapping，SLAM）作为移动机器人的基础，为其自主导航与安全避障提供了可靠支撑.针对大规模地下空间传感器退化、计算瓶颈以及移动机器人感知范围受限且累积漂移严重等挑战，提出了超宽带（Ultra-WideBand，UWB）紧耦合的多智能体端边协作SLAM方法（Robots-edge Collaborative SLAM，Re-CoSLAM）.本文在边缘辅助的多模态SLAM框架基础上，结合误差状态卡尔曼滤波，设计了UWB紧耦合绝对位姿估计方法，有效提升了全局定位性能.进一步，基于UWB全局定位，建立了可扩展的多智能体协同SLAM框架与自适应传输机制.为了保障全局一致性，根据多智能体之间的UWB距离测量，提出了相对距离约束的联合位姿图优化方法.此外，考虑到边缘节点的计算瓶颈问题，设计基于请求优先级的任务调度策略，以减少排队延迟并提高跟踪精度.本文在3台搭载英伟达板载计算机的移动机器人和1台边缘计算节点上部署Re-CoSLAM，并在室内走廊、地下车库与地下巷道场景下开展了广泛的实验与评估.结果表明，Re-CoSLAM可实现7.3 cm的绝对定位精度与13 帧/秒的运行速度，定位误差比现有方法降低了50%以上.

Abstract

With the development of underground exploration towards deep

large

and unmanned

mobile robots have become crucial in underground detection and rescue. As the basis of mobile robots

simultaneous localization and mapping (SLAM) provides reliable support for its autonomous navigation and obstacle avoidance. Due to the sensors degradation

the constrained computing resources

and the limited sensing range and serious cumulative drift of mobile robots in large-scale underground environments

a robots-edge collaborative SLAM (Re-CoSLAM) method via ultra-wideband (UWB) tightly-coupled is proposed. Based on the edge assisted multi-modal SLAM framework

Re-CoSLAM designs a UWB tightly-coupled absolute pose estimation method based on the error state Kalman filter to improve absolute localization performance. Combined with the UWB absolute localization

a scaling up multi-agent collaborative SLAM framework and a adaptive transmission mechanism are further established. To ensure global consistency

Re-CoSLAM proposes a joint pose graph optimization algorithm with UWB relative range constraints between the multiple agents. Besides

considering the constrained computing resources of the edge server

a task scheduling strategy based on request priority is devised to reduce queuing latency and improve tracking accuracy. In this paper

Re-CoSLAM is fully deployed on three mobile robots equipped with NVIDIA on-board computers and an edge server

and extensive experiments and evaluations are performed in the indoor corridor

underground garage and underground tunnel scenarios. The results indicate that Re-CoSLAM can achieve an absolute localization accuracy of 7.3 cm and a speed of 13 Frames Per Second in various scenarios

with localization errors reduced by more than 50% compared to existing solutions.

关键词

Keywords

references

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