Heterogeneous Graph Network-Based UWB Localization for Complex Indoor Environments

Accurate indoor location-based services are important for mobile robots, especially in complex indoor environments. In this paper, we propose a heterogeneous graph network-based ultra-wide band (UWB) localization method to provide accurate and robust localization results for mobile robots in complex indoor scenarios. The core of our approach lies in constructing the anchors, ranging measurements and tags into a heterogeneous graph structure according to the topological structure of the UWB localization system, and then design a spatial-temporal heterogeneous graph attention neural network to extract high-level features and estimate the tag locations from the graph. Therefore, the geometric relationships contained in the UWB localization system are comprehensively established, while the spatial and temporal information contained in the ranging measurements can also be extracted. We validate the proposed method through real-world experiments. The results demonstrate that, compared to existing deep learning-based methods, the constructed heterogeneous graph better represents the geometric structure of the UWB localization system, and the designed heterogeneous graph neural network effectively extracts the spatial-temporal and geometric features. Consequently, the accuracy and robustness of UWB localization are significantly improved.

Authors

• Bo Yang
• Luyang Li
• Sizhen He
• Weinan Chen
• Hong Zhang

Keywords

• Location awareness
• Learning systems
• Accuracy
• Feature extraction
• Distance measurement
• Robustness
• Graph neural networks
• Indoor environment
• Mobile robots
• Intelligent robots
• Indoor Environments
• Ultra-wideband
• Heterogeneous Graph
• Complex Indoor Environments
• Ultra-wideband Localization
• Neural Network
• Localization Accuracy
• Geometric Features
• High-level Features
• Graph Structure
• Heterogeneous Network
• Real-world Experiments
• Deep Learning-based Methods
• Mobile Robot
• Geometric Relationship
• Extract High-level Features
• Robust Localization
• Tagging Location
• Convolutional Neural Network
• Node Features
• Received Signal Strength Indicator
• Improve Localization Accuracy
• Types Of Nodes
• Experimental Environment
• Types Of Edges
• Graph Convolutional Network
• Temporal Relationship
• Unscented Kalman Filter
• Feature Aggregation