Resource-Aware Collaborative Monte Carlo Localization with Distribution Compression

Global localization is essential in enabling robot autonomy, and collaborative localization is key for multi-robot systems, allowing for more efficient planning and execution of tasks. In this paper, we address the task of collaborative global localization under computational and communication constraints. We propose a method which reduces the amount of information exchanged and the computational cost. We also analyze, implement and open-source seminal approaches, which we believe to be a valuable contribution to the community. We exploit techniques for distribution compression in near-linear time, with error guarantees. We evaluate our approach and the implemented baselines on multiple challenging scenarios, simulated and real-world. Our approach can run online on an onboard computer. We release an open-source C++/ROS2 implementation of our approach, as well as the baselines. 1

Authors

• Nicky Zimmerman
• Alessandro Giusti
• Jérôme Guzzi

Keywords

• Location awareness
• Monte Carlo methods
• Collaboration
• Planning
• Computational efficiency
• Complexity theory
• Multi-robot systems
• Intelligent robots
• Monte Carlo Localization
• Computational Cost
• Information Exchange
• Multi-agent Systems
• Global Localization
• Onboard Computer
• Sample Set
• K-means
• Time Complexity
• Simulation Environment
• Detection Model
• Geometric Features
• Particle Filter
• Pose Estimation
• Representative Points
• Odometry
• Naive Approach
• Convergence Threshold
• Bandwidth Requirements
• Degree Of Symmetry
• Maximum Mean Discrepancy
• Naive Implementation
• Single Robot
• Compression Scheme
• Message Size
• Ground Truth Pose
• Local Failure
• Seminal Work
• Delocalization