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Collaborative 3D Scene Reconstruction in Large Outdoor Environments Using a Fleet of Mobile Ground Robots.

John Lewis¹, Pedro U Lima¹, Meysam Basiri¹

¹Institute for Systems and Robotics, Instituto Superior Tecnico, University of Lisbon, 1049-001 Lisbon, Portugal.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This study presents a decentralized framework for mobile robots to collaboratively create 3D maps of large outdoor areas. The system efficiently shares local maps using limited communication, enhancing situational awareness for operations like precision agriculture.

Keywords:

3D mapping communication constraint cooperative mapping multi-robot system point cloud registration scene reconstruction

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Area of Science:

Robotics and Autonomous Systems
Geospatial Mapping
Sensor Fusion

Background:

Outdoor robotic applications require efficient large-area exploration.
Limited communication range and bandwidth pose challenges for collaborative mapping.
Existing methods often struggle with decentralized, real-time 3D mapping in dynamic environments.

Purpose of the Study:

To develop an active and decentralized framework for collaborative 3D mapping of large outdoor areas.
To enable mobile robot teams to build a global environmental representation under communication constraints.
To enhance situational awareness for operators in outdoor robotic missions.

Main Methods:

Utilized a team of mobile ground robots equipped with 3D LiDAR sensors.
Implemented a real-time method for sharing and registering individual local maps.
Employed a conditional peer-to-peer communication strategy for efficient data exchange over varying distances and bandwidths.

Main Results:

Successfully generated a global 3D map from decentralized local maps.
Demonstrated reliability and efficiency in both real-world (solar power plant) and simulated (digital twin) experiments.
Validated the framework's performance under limited communication range and bandwidth conditions.

Conclusions:

The proposed decentralized framework is effective for collaborative 3D mapping in large outdoor environments.
The system enhances operational efficiency and situational awareness for teams of mobile robots.
The approach is robust and scalable for applications in precision agriculture, search and rescue, and industrial inspection.