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NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio.

Panagiotis T Karfakis1,2, Micael S Couceiro1,2, David Portugal2

  • 1Ingeniarius Ltd., R. Nossa Sra. Conceição 146, 4445-147 Alfena, Portugal.

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Summary
This summary is machine-generated.

This study introduces a 5G New Radio (NR) based cellular Simultaneous Localization and Mapping (SLAM) framework for robot localization. It offers a robust alternative to Global Navigation Satellite Systems (GNSS) and LiDAR methods in challenging outdoor environments.

Keywords:
5G NRREMSLAMfield roboticsiSAMpose estimationradio mappingsensor fusion

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Area of Science:

  • Robotics
  • Wireless Communication
  • Sensor Fusion

Background:

  • Robot localization is essential for navigation but faces limitations with Global Navigation Satellite Systems (GNSS) in urban/rural areas.
  • LiDAR, inertial, and visual methods are prone to drift and environmental/illumination condition variations.
  • Existing localization techniques often lack robustness in diverse outdoor scenarios.

Purpose of the Study:

  • To propose a novel cellular Simultaneous Localization and Mapping (SLAM) framework for mobile robot localization.
  • To leverage 5G New Radio (NR) signals and inertial measurements for enhanced localization accuracy.
  • To create a radio signal map using Received Signal Strength Indicator (RSSI) for pose correction.

Main Methods:

  • Developed a cellular SLAM framework integrating 5G NR signals and inertial measurement units (IMUs).
  • Utilized gNodeB stations for signal triangulation and Received Signal Strength Indicator (RSSI) mapping.
  • Benchmarked the proposed method against LiDAR-Inertial Odometry Smoothing and Mapping (LIO-SAM) using a simulator and real-world experiments.
  • Evaluated performance across sub-6 GHz and mmWave frequency bands using down-link (DL) signals.

Main Results:

  • The 5G-based cellular SLAM framework demonstrated increased robustness in outdoor environments.
  • The system successfully generated a radio signal map for pose correction.
  • Performance was validated against a state-of-the-art LiDAR SLAM method.
  • The proposed method showed potential as an supplementary absolute positioning source.

Conclusions:

  • 5G positioning is viable for radio SLAM, enhancing robot localization capabilities.
  • The framework provides a reliable alternative when GNSS is unavailable or LiDAR/IMU methods degrade.
  • This approach offers improved robustness and accuracy for mobile robots in outdoor settings.