A laser SLAM-based control method for firefighting robots

By improving the Cartographer SLAM and Lazy Decision algorithms, and combining LiDAR and cameras, loop closure detection is optimized to achieve autonomous path planning and precise positioning of the firefighting robot. This solves the problem of the firefighting robot having limited functionality in building fires, improves its autonomy and positioning accuracy, and assists firefighters in rescue operations.

CN116337069BActive Publication Date: 2026-03-10YANCHENG INST OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-03-09
Publication Date
2026-03-10

AI Technical Summary

Technical Problem

Existing firefighting robots have limited functionality in building fires, outdated control strategies, and are unable to autonomously detect unknown environments, relying on manual operation. Furthermore, their mapping systems have poor anti-interference capabilities, resulting in inaccurate positioning and an inability to efficiently assist firefighters in rescue operations.

Method used

By employing an improved Cartographer SLAM algorithm combined with the Lazy Decision algorithm, feature points are extracted using LiDAR and cameras to build an environmental model, optimize loop closure detection, and utilize a tracked chassis and infrared sensors to achieve autonomous path planning and precise positioning. It is also equipped with a ranging sensor for real-time fire suppression.

Benefits of technology

It improves the autonomy and positioning accuracy of firefighting robots, reduces computational load and external interference, and enables autonomous detection and firefighting without human intervention, assisting in rescue and reducing reliance on manual operation.

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Abstract

This invention proposes a firefighting robot control method based on laser SLAM. The robot first uses Cartographer's SLAM algorithm to accurately extract image features from the disaster site, continuously building and refining the disaster site model based on these features. After receiving a firefighting mission, the robot first identifies flames based on information uploaded by its onboard depth camera. Once the flame source is identified, it actively plans a rescue route to reach the fire area as quickly as possible. Simultaneously, it uses its onboard binocular camera and LiDAR to avoid obstacles along the way, while infrared and distance sensors accurately identify the fire source, ensuring the robot quickly understands the situation on-site and can begin firefighting immediately upon arrival. This algorithm is based on an improved SLAM algorithm by Cartographer, incorporating a Lazy Decision algorithm in the loop closure detection part to optimize the Cartographer algorithm, effectively avoiding the consequences of loop closure errors and preventing incorrect loop closures.
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