A Field-adaptive Multi-robot Collaborative Real-time Mapping System

TANG Jie; YANG Di; JIANG Kai; LIU Shaoshan

doi:10.12382/bgxb.2025.0655

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A Field-adaptive Multi-robot Collaborative Real-time Mapping System

更新时间：2026-06-16

- A Field-adaptive Multi-robot Collaborative Real-time Mapping System
- Acta Armamentarii Vol. 47, Issue 5, Pages: 250655(2026)
- 作者机构：
  
  华南理工大学计算机科学与工程学院，广东广州 510006
  深圳市人工智能与机器人研究院，广东深圳 518172
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0655
  CLC： TP242
- Received：16 July 2025，
  
  Online First：11 February 2026，
  
  Published：2026-05
- 稿件说明：
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TANG Jie, YANG Di, JIANG Kai, et al. A Field-adaptive Multi-robot Collaborative Real-time Mapping System[J]. Acta Armamentarii, 2026, 47(5): 250655.
DOI：

TANG Jie, YANG Di, JIANG Kai, et al. A Field-adaptive Multi-robot Collaborative Real-time Mapping System[J]. Acta Armamentarii, 2026, 47(5): 250655. DOI： 10.12382/bgxb.2025.0655.

摘要

在复杂多变、非结构化的野外战场环境中，实现多无人系统协同建图是支撑智能作战单元任务执行、态势感知与安全保障的关键能力。面对现有系统在环境动态性强、通信受限、平台异构、地图融合效率低等挑战，提出一种面向战术任务的高自适应多机器人协同实时制图系统。该系统围绕战场通信资源稀缺与跨平台感知协同需求，设计了轻量化的数据压缩与高效消息传输机制，实现低带宽条件下异构平台间位姿与观测信息的高效同步。针对地图一致性问题，引入冗余感知驱动的回环检测策略以触发协同地图融合，并结合控制参数驱动的截断最小二乘优化模型与渐进非凸性求解方法，有效抑制异常回环干扰。系统还支持按需调用的全局光束法平差优化模块，显著提升地图精度与稳定性。实验结果表明：该系统可在野外数平方公里作战区域内，仅依赖间歇性低带宽链路，即可实现厘米级相对定位精度与全局地图一致性；在地图数据压缩50%以上的条件下，定位误差平均仅增加5.7 mm，显著增强了无人集群在复杂环境下的自主作业与协同感知能力，为智能化战场感知体系建设提供了关键技术支撑。

Abstract

In complex and unstructured battlefield environments，the collaborative mapping of multiple unmanned systems is vital for the mission execution，situational awareness and operational safety of intelligent combat units. A field-adaptive multi-robot collaborative real-time mapping system is presented to meet the demands of tactical operations under the constraints of low-bandwidth，high-dynamics and heterogeneous platform. The system features the lightweight data compression and efficient cross-platform message passing to enable the real-time pose and observation synchronization under intermittent communications. A redundancy-aware loop closure strategy is introduced to drive inter-robot map fusion，and the abnormal loop interference is suppressed by using a control parameter-driven truncated least squares optimization model and a progressive non-convex solution method. An on-demand global bundle adjustment optimization module is used to further improve the accuracy and stability of map. The experimental results show that the system maintains centimeter-level relative localization accuracy and global map consistency over multi-square-kilometer areas. The positioning error is increased by only 5.7 mm under the condition of compressing the map data by more than 50% . These results demonstrate that the system can be used as a core sensing infrastructure for autonomous unmanned swarms in future intelligent combat systems.

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