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

TL;DR

TWC-SLAM is a multi-agent cooperative SLAM framework that enhances indoor localization and mapping accuracy by integrating text semantics and WiFi signals, especially in environments with repetitive structures.

Contribution

It introduces a novel integration of text semantics and WiFi features into cooperative SLAM to improve location identification and loop closure detection in similar indoor environments.

Findings

Significantly improves SLAM accuracy in repetitive indoor environments

Effectively utilizes text and WiFi data for location recognition

Enhances global map consistency through loop closure detection

Abstract

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.