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Neural Network-based Multiple Robot Simultaneous Localization And Mapping.

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Neural Network-based Multiple Robot Simultaneous Localization And Mapping.

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Neural network-based multiple robot simultaneous localization and mapping.

Sajad Saeedi¹, Liam Paull, Michael Trentini

¹Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 9P8, Canada. sajad.saeedi.g@unb.ca

IEEE Transactions on Neural Networks

|December 14, 2011

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a decentralized platform for multi-robot simultaneous localization and mapping (SLAM). It uses a novel occupancy grid map fusion algorithm with unsupervised neural network clustering for effective robot navigation.

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Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Simultaneous Localization and Mapping (SLAM) is crucial for autonomous robot navigation.
Extending single-robot SLAM to multi-robot systems presents significant challenges in map fusion and coordination.

Purpose of the Study:

To develop a decentralized platform for multi-robot simultaneous localization and mapping (SLAM).
To propose a novel occupancy grid map fusion algorithm for enhanced multi-robot coordination.

Main Methods:

Each robot employs view-based SLAM with an extended Kalman filter, fusing encoder and laser ranger data.
A multistep map fusion algorithm is introduced, including neural network-based clustering for map learning.
Relative orientation and translation are extracted using norm histogram cross-correlation, Radon transform, and matching norm vectors.

Main Results:

The proposed self-organizing map-based clustering effectively learns map features for unsupervised, on-the-fly map fusion.
The system successfully extracts relative poses between robots for accurate map merging.
Experimental results in a real environment demonstrate the effectiveness of the multi-robot SLAM solution.

Conclusions:

The developed decentralized platform enables efficient multi-robot SLAM.
The novel map fusion algorithm, leveraging unsupervised learning, significantly improves multi-robot localization and mapping accuracy.
The approach offers a robust solution for complex robotic navigation tasks.