Mr. Virgil: Learning Multi-robot Visual-range Relative Localization

Ultra-wideband (UWB)-vision fusion localization has achieved extensive applications in the domain of multiagent relative localization. The challenging matching problem between robots and visual detection renders existing methods highly dependent on identity-encoded hardware or delicate tuning algorithms. Overconfident yet erroneous matches may bring about irreversible damage to the localization system. To address this issue, we introduce Mr. Virgil, an end-to-end learning multi-robot visual-range relative localization framework, consisting of a graph neural network for data association between UWB rangings and visual detections, and a differentiable pose graph optimization (PGO) back-end. The graph-based front-end supplies robust matching results, accurate initial position predictions, and credible uncertainty estimates, which are subsequently integrated into the PGO back-end to elevate the accuracy of the final pose estimation. Additionally, a decentralized system is implemented for real-world applications. Experiments spanning varying robot numbers, simulation and real-world, occlusion and non-occlusion conditions showcase the stability and exactitude under various scenes compared to conventional methods. Our code is available at: https://github.com/HiOnes/Mr-Virgil.

Authors

• Si Wang
• Zhehan Li
• Jiadong Lu
• Rong Xiong
• Yanjun Cao
• Yue Wang

Keywords

• Location awareness
• Visualization
• Uncertainty
• Accuracy
• Graph neural networks
• Stability analysis
• Optimization
• Tuning
• Visual odometry
• Ultra wideband technology
• Neural Network
• Uncertainty Estimation
• Visual Detection
• Ranging
• Pose Estimation
• Decentralized System
• Graph Optimization
• F1 Score
• Precision And Recall
• Global Positioning System
• Multilayer Perceptron
• Visual Observation
• Position Weight Matrices
• Depth Camera
• Multi-agent Systems
• Recall Rate
• Matching Score
• Physical Experiments
• Local Frame
• Graph Matching
• Simple Matching
• Relative Pose
• Robot Operating System
• Simulated Scene
• Vision-based Methods
• Convolutional Neural Network
• Prior Set