TWC-SLAM: テキスト意味論とWiFi特徴を統合した類似屋内環境向けマルチエージェント協調SLAM
TWC-SLAM: Multi-Agent Cooperative SLAM with Text Semantics and WiFi Features Integration for Similar Indoor Environments
類似した屋内環境でマルチエージェント協調SLAMの位置同定精度を向上させるため、テキスト意味論とWiFi信号特徴を統合したフレームワークTWC-SLAMを提案し、ループ閉じ込み検出と地図統合を改善した。
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著者: Chunyu Li, Shoubin Chen, Dong Li, Weixing Xue, Qingquan Li
分類: cs.RO
原文アブストラクト
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.
