Literature DB >> 31752251

A Fire Reconnaissance Robot Based on SLAM Position, Thermal Imaging Technologies, and AR Display.

Sen Li1, Chunyong Feng1, Yunchen Niu1, Long Shi2, Zeqi Wu1, Huaitao Song1.   

Abstract

Due to hot toxic smoke and unknown risks under fire conditions, detection and relevant reconnaissance are significant in avoiding casualties. A fire reconnaissance robot was therefore developed to assist in the problem by offering important fire information to fire fighters. The robot consists of three main systems, a display operating system, video surveillance, and mapping and positioning navigation. Augmented reality (AR) goggle technology with a display operating system was also developed to free fire fighters' hands, which enables them to focus on rescuing processes and not system operation. Considering smoke disturbance, a thermal imaging video surveillance system was included to extract information from the complicated fire conditions. Meanwhile, a simultaneous localization and mapping (SLAM) technology was adopted to build the map, together with the help of a mapping and positioning navigation system. This can provide a real-time map under the rapidly changing fire conditions to guide the fire fighters to the fire sources or the trapped occupants. Based on our experiments, it was found that all the tested system components work quite well under the fire conditions, while the video surveillance system produces clear images under dense smoke and a high-temperature environment; SLAM shows a high accuracy with an average error of less than 3.43%; the positioning accuracy error is 0.31 m; and the maximum error for the navigation system is 3.48%. The developed fire reconnaissance robot can provide a practically important platform to improve fire rescue efficiency to reduce the fire casualties of fire fighters.

Entities:  

Keywords:  AR; SLAM; fire reconnaissance robot; thermal imaging

Year:  2019        PMID: 31752251     DOI: 10.3390/s19225036

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  1 in total

1.  An Improved Near-Field Computer Vision for Jet Trajectory Falling Position Prediction of Intelligent Fire Robot.

Authors:  Jinsong Zhu; Lu Pan; Ge Zhao
Journal:  Sensors (Basel)       Date:  2020-12-08       Impact factor: 3.576

  1 in total

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