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Fire SLAM: a visual Simultaneous Localization and Mapping algorithm for firefighting robots

Published online by Cambridge University Press:  27 October 2025

Tao Yang ,
Weili Ding Open the ORCID record for Weili Ding [Opens in a new window]  and
Junjie Luo
Tao Yang
Affiliation:
The Engineering Research Center of Intelligent Control System and Intelligent Equipment, Ministry of Education, Hebei Key Laboratory of Intelligent rehabilitation and Neuromodulation, Hebei Advanced Equipment Industry Technology Research Institute, Yanshan University, Qinhuangdao, 066004, China
Weili Ding*
Affiliation:
The Engineering Research Center of Intelligent Control System and Intelligent Equipment, Ministry of Education, Hebei Key Laboratory of Intelligent rehabilitation and Neuromodulation, Hebei Advanced Equipment Industry Technology Research Institute, Yanshan University, Qinhuangdao, 066004, China
Junjie Luo
Affiliation:
The Engineering Research Center of Intelligent Control System and Intelligent Equipment, Ministry of Education, Hebei Key Laboratory of Intelligent rehabilitation and Neuromodulation, Hebei Advanced Equipment Industry Technology Research Institute, Yanshan University, Qinhuangdao, 066004, China
*
Corresponding author: Weili Ding; Email: weiye51@ysu.edu.cn
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Abstract

In firefighting missions, human firefighters are often exposed to high-risk environments such as intense heat and limited visibility. To address this, firefighting robots can serve as valuable agents for autonomous navigation and flame perception. This paper proposes a novel visual Simultaneous Localization and Mapping (SLAM) framework, Fire SLAM, tailored for firefighting scenarios. The system integrates a flame detection and tracking thread-based on the YOLOv8n network and Kalman filtering-to achieve real-time flame detection, tracking, and 3D localization. By leveraging the detection results, dynamic flame regions are excluded from the SLAM front-end, allowing static features to be used for robust pose estimation and loop closure. To validate the proposed system, multiple datasets were collected from real-world and simulated fire environments. Experimental results demonstrate that Fire SLAM improves localization accuracy and robustness in fire scenes with flame disturbances, showing promise for autonomous firefighting robot deployment.

Keywords

firefighting robotsflame detectionflame localizationSLAM

Information

Type
Research Article
Information
Robotica , First View , pp. 1 - 17
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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