arXiv 2510.227542025-10-26cs.RO

TWC-SLAM：面向相似室内环境的多智能体协同SLAM，融合文本语义与WiFi特征

TWC-SLAM: Multi-Agent Cooperative SLAM with Text Semantics and WiFi Features Integration for Similar Indoor Environments

Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东人工智能与数字经济实验室）；Shenzhen University（深圳大学）；College of natural resources and environment（自然资源与环境学院）

Chunyu Li, Shoubin Chen, Dong Li, Weixing Xue, Qingquan Li

备注：: Accepted by the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2025

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AI总结：

针对相似室内环境下多智能体协同SLAM定位不准的问题，提出融合文本语义与WiFi特征的TWC-SLAM框架，通过增强位置识别与回环检测，显著提升复杂重复环境下的建图性能。

摘要：

多智能体协同SLAM在具有重复结构（如走廊和房间）的相似室内环境中常常面临挑战。当采用基于点云的技术时，这些挑战可能导致共享位置识别出现显著误差。为缓解这些问题，我们提出了TWC-SLAM，一种多智能体协同SLAM框架，该框架融合文本语义和WiFi信号特征以增强位置识别和回环检测。TWC-SLAM包含基于FAST-LIO2的单智能体前端里程计模块、利用文本语义和WiFi特征的位置识别与回环检测模块，以及全局建图模块。智能体配备有能够捕获文本信息和检测WiFi信号的传感器。通过关联这些数据源，TWC-SLAM建立共同位置，促进不同智能体地图间的点云对齐。此外，系统采用回环检测和优化模块以实现全局优化和连贯建图。我们使用一个包含相似走廊、房间和文本标识的室内数据集评估了我们的方法。结果表明，TWC-SLAM显著提升了协同SLAM系统在具有重复建筑特征的复杂环境中的性能。

英文摘要：

Multi-agent cooperative SLAM often encounters challenges in similar indoor environments characterized by repetitive structures, such as corridors and rooms. These challenges can lead to significant inaccuracies in shared location identification when employing point cloud-based techniques. To mitigate these issues, we introduce TWC-SLAM, a multi-agent cooperative SLAM framework that integrates text semantics and WiFi signal features to enhance location identification and loop closure detection. TWC-SLAM comprises a single-agent front-end odometry module based on FAST-LIO2, a location identification and loop closure detection module that leverages text semantics and WiFi features, and a global mapping module. The agents are equipped with sensors capable of capturing textual information and detecting WiFi signals. By correlating these data sources, TWC-SLAM establishes a common location, facilitating point cloud alignment across different agents' maps. Furthermore, the system employs loop closure detection and optimization modules to achieve global optimization and cohesive mapping. We evaluated our approach using an indoor dataset featuring similar corridors, rooms, and text signs. The results demonstrate that TWC-SLAM significantly improves the performance of cooperative SLAM systems in complex environments with repetitive architectural features.

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