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Deep Compressed Communication and Application in Multi-Robot 2D-Lidar SLAM: An Intelligent Huffman Algorithm.

Liang Zhang¹, Jinghui Deng¹

¹School of Electrical Engineering and Automation, Anhui University, Hefei 230093, China.

Sensors (Basel, Switzerland)

|May 25, 2024

Summary

This study introduces a communication-efficient framework for multi-robot Simultaneous Localization and Mapping (SLAM) using compressed 2D maps. The novel approach significantly reduces bandwidth by 99% while preserving map quality for collaborative navigation.

Keywords:

2D-lidar SLAM Huffman encoder communication-limited application deep compressed network multi-robot system

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Multi-robot Simultaneous Localization and Mapping (SLAM) systems are crucial for navigation in environments without Global Navigation Satellite System (GNSS) coverage.
Scalability issues in large-scale multi-robot SLAM arise from high memory and communication bandwidth requirements for 2D maps.
Data compression is essential to overcome bandwidth limitations in these systems.

Purpose of the Study:

To investigate communication-efficient multi-robot SLAM using compressed 2D maps.
To introduce an architecture enabling the transmission of full maps with reduced bandwidth.
To address scalability and bandwidth constraints in collaborative SLAM.

Main Methods:

A framework utilizing a lightweight Convolutional Neural Network (CNN) for feature extraction from 2D maps.
An encoder combining Huffman and Run-Length Encoding (RLE) for map data compression.
A lightweight recovery CNN designed to restore map features post-transmission.

Main Results:

Experimental validation on a two-robot SLAM system demonstrated a 99% reduction in communication overhead.
The proposed method effectively maintained map quality despite significant compression.
The framework successfully addressed bandwidth constraints in multi-robot SLAM.

Conclusions:

The developed compressed communication strategy offers a practical solution for bandwidth limitations in multi-robot SLAM.
This approach enhances the scalability of collaborative SLAM applications.
Enables efficient exploration and navigation in GNSS-limited environments.