Non-Overlap-Aware Egocentric Pose Estimation for Collaborative Perception in Connected Autonomy

Egocentric pose estimation is a fundamental capability for multi-robot collaborative perception in connected autonomy, such as connected autonomous vehicles. During multi-robot operations, a robot needs to know the relative pose between itself and its teammates with respect to its own coordinates. However, different robots usually observe completely different views that contains similar objects, which leads to wrong pose estimation. In addition, it is unrealistic to allow robots to share their raw observations to detect overlap due to the limited communication bandwidth constraint. In this paper, we introduce a novel method for Non-Overlap-Aware Egocentric Pose Estimation (NOPE), which performs egocentric pose estimation in a multi-robot team while identifying the non-overlap views and satifying the communication bandwidth constraint. NOPE is built upon an unified hierarchical learning framework that integrates two levels of robot learning: (1) high-level deep graph matching for correspondence identification, which allows to identify if two views are overlapping or not, (2) low-level position-aware cross-attention graph learning for egocentric pose estimation. To evaluate NOPE, we conduct extensive experiments in both high-fidelity simulation and real-world scenarios. Experimental results have demonstrated that NOPE enables the novel capability for non-overlapping-aware egocentric pose estimation and achieves state-of-art performance compared with the existing methods.

Authors

• Hong Huang
• Dongkuan Xu
• Hao Zhang
• Peng Gao

Keywords

• Deep learning
• Filters
• Costs
• Robot kinematics
• Pose estimation
• Collaboration
• Bandwidth
• Robot learning
• Multi-robot systems
• Intelligent robots
• Egocentric Pose
• Team Sports
• Autonomous Vehicles
• Real-world Scenarios
• Similar Objects
• Limited Bandwidth
• Hierarchical Framework
• High-fidelity Simulation
• Communication Bandwidth
• Relative Pose
• Graph Matching
• Visual Features
• Pedestrian
• Multilayer Perceptron
• Similarity Matrix
• Cyanoacrylate
• Multi-agent Systems
• Global Navigation Satellite System
• Position Estimation
• Node Embeddings
• Simultaneous Localization And Mapping
• Swarm Robotics
• Root Mean Square Error Of Cross-validation
• Position Embedding
• Feature Matching
• Ground Truth Position
• Loop Closure
• Attention Gate
• Situational Awareness