Multi-Robot Collaborative Perception with Graph Neural Networks

Abstract

Multi-robot systems such as swarms of aerial robots are naturally suited to offer additional flexibility, resilience, and robustness in several tasks compared to a single robot by enabling cooperation among the agents. To enhance the autonomous robot decision-making process and situational awareness, multi-robot systems have to coordinate their perception capabilities to collect, share, and fuse environment information among the agents in an efficient and meaningful way such to accurately obtain context-appropriate information or gain resilience to sensor noise or failures. In this paper, we propose a general-purpose Graph Neural Network (GNN) with the main goal to increase, in multi-robot perception tasks, single robots' inference perception accuracy as well as resilience to sensor failures and disturbances. We show that the proposed framework can address multi-view visual perception problems such as monocular depth estimation and semantic segmentation. Several experiments both using photo-realistic and real data gathered from multiple aerial robots' viewpoints show the effectiveness of the proposed approach in challenging inference conditions including images corrupted by heavy noise and camera occlusions or failures.

arXiv comment8 pages, 10 figures, 3 tables, Accepted at the IEEE Robotics Automation Letter (RAL) and the IEEE International Conference on Robotics and Automation (ICRA), 2022

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• Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
• Semantics for Robotic Mapping, Perception and Interaction: A Survey
• Flightmare: A Flexible Quadrotor Simulator
• Graph Neural Networks for Motion Planning
• Pose-GNN : Camera Pose Estimation System Using Graph Neural Networks

Cited by in corpus (7)

• Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges
• A Survey on Collaborative DNN Inference for Edge Intelligence
• Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
• TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation
• Input State Stability of Gated Graph Neural Networks
• Nav-SCOPE: Swarm Robot Cooperative Perception and Coordinated Navigation
• Collaborative Goal Tracking of Multiple Mobile Robots Based on Geometric Graph Neural Network