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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Related Experiment Video

Updated: May 14, 2025

Design and Analysis for Fall Detection System Simplification
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Robust Multi-Sensor Fusion for Localization in Hazardous Environments Using Thermal, LiDAR, and GNSS Data.

Lukas Schichler1, Karin Festl1, Selim Solmaz1

  • 1Virtual Vehicle Research GmbH, 8010 Graz, Austria.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
Summary

This study presents a robust sensor fusion algorithm for autonomous robot navigation in hazardous areas. The system reliably localizes robots using thermal cameras, LiDAR, and GNSS, even with sensor failures.

Keywords:
extended Kalman filter (EKF)robust localizationsensor fusionthermal camera odometry

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Area of Science:

  • Robotics and Autonomous Systems
  • Sensor Fusion and Localization
  • Hazardous Environment Navigation

Background:

  • Autonomous robots are crucial for search and rescue in hazardous environments like tunnels and disaster areas.
  • Sensor failures and errors significantly challenge localization accuracy in these demanding conditions.
  • Existing localization methods often struggle with the unreliability of individual sensors in complex terrains.

Purpose of the Study:

  • To develop a robust sensor fusion algorithm for reliable autonomous robot localization in hazardous environments.
  • To integrate data from thermal cameras, LiDAR, and Global Navigation Satellite Systems (GNSS) for enhanced positioning.
  • To ensure continuous localization performance despite individual sensor outages or data compromises.

Main Methods:

  • Implemented distinct Simultaneous Localization and Mapping (SLAM) and odometry techniques for thermal and LiDAR sensors.
  • Utilized an Extended Kalman Filter (EKF) to fuse data from thermal camera, LiDAR, and GNSS.
  • Accommodated varying sensor sampling rates and simulated single-sensor outages during field testing.

Main Results:

  • The proposed sensor fusion algorithm demonstrated reliable localization performance in challenging urban environments.
  • The system effectively compensated for individual sensor failures, maintaining localization accuracy.
  • Field tests validated the algorithm's robustness under simulated real-world conditions with sensor outages.

Conclusions:

  • Robust sensor fusion is essential for dependable autonomous navigation in hazardous and unpredictable settings.
  • Integrating thermal, LiDAR, and GNSS data via an EKF provides a resilient localization solution.
  • The developed algorithm offers a promising approach for enhancing the safety and effectiveness of autonomous robots in critical operations.