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An Efficient Rescue System with Online Multi-Agent SLAM Framework.

SeungHwan Lee1, HanJun Kim2, BeomHee Lee2

  • 1Department of Electronic Engineering, Kumoh National Institute of Technology, Gumi, Gyeongbuk 39177, Korea.

Sensors (Basel, Switzerland)
|January 8, 2020
PubMed
Summary

This study introduces a multi-agent simultaneous localization and mapping (SLAM) system to speed up building rescues. The efficient framework creates accurate merged maps, improving victim search operations in emergencies.

Keywords:
SLAMmap mergingmulti-agent SLAMrescue systemtruncated signed distance

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Emergency Response Systems

Background:

  • Rapid urban fires necessitate efficient search and rescue operations.
  • Current mapping technologies face challenges in dynamic and complex indoor environments.
  • Effective localization and mapping are critical for coordinating rescue teams.

Purpose of the Study:

  • To develop an efficient multi-agent simultaneous localization and mapping (SLAM) framework for reducing rescue times in burning buildings.
  • To enhance the accuracy and efficiency of map construction and merging for search and rescue operations.
  • To enable rescuers to utilize a collaboratively built map for effective victim location.

Main Methods:

  • Implementation of a truncated signed distance (TSD)-based SLAM algorithm for accurate 2D map construction.
  • Integration of the general iterative closest point (GICP) method for handling novel environmental scenarios.
  • Development of an online map merging strategy with adaptive weighting for combining individual agent maps.
  • Utilizing a smart helmet equipped with light-detection and ranging (LiDAR) and inertial measurement unit (IMU) sensors.

Main Results:

  • The proposed SLAM framework demonstrated more accurate map construction compared to conventional TSD-SLAM.
  • Online map merging produced a more correct and accurate merged map through optimized parameter determination.
  • The system successfully generated a unified map from multiple agents, enhancing situational awareness.

Conclusions:

  • The developed multi-agent SLAM system significantly improves mapping accuracy and efficiency in simulated fire rescue scenarios.
  • Accurate merged maps facilitate faster and more effective victim searches by rescue personnel.
  • This framework offers a promising solution for enhancing safety and operational effectiveness in building emergency response.