Multi-Robot Collaborative Perception With Graph Neural Networks

Yang Zhou, Jiuhong Xiao, Yue Zhou, Giuseppe Loianno

Research output: Contribution to journalArticlepeer-review


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 efficiently to obtain context-appropriate information or gain resilience to sensor noise or failures. In this letter, 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.

Original languageEnglish (US)
Pages (from-to)2289-2296
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Apr 1 2022


  • Encoding
  • Estimation
  • Robot kinematics
  • Robot sensing systems
  • Robots
  • Semantics
  • Task analysis

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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