Abstract
Traditional indoor navigation algorithms generally only consider the geometrical information of indoor space. However, the environmental information and semantic parameters of a fire are also important for evacuation routing in the case of a fire. It is difficult for traditional indoor navigation algorithms to dynamically find an indoor path when a fire develops. To address this problem, we developed a multi-semantic constrained three-dimensional (3D) indoor fire evacuation routing method that considers multi-dimensional indoor fire scene-related semantics, such as path accessibility, path recognition degree, and fire parameters. Our method enhances the navigation semantics of indoor space by extending the fire-related components of indoor model based on IndoorGML and integrating location semantics of IndoorLocationGML. We also propose quantifiable indoor fire-oriented routing semantics and establish a navigation cost function that evaluates semantic changes during a fire. We designed an indoor routing algorithm with multiple semantic constraints based on the A* algorithm. The indoor routing results were analyzed and compared in simulation experiments. The experimental results showed that the proposed model can remove unusable nodes and edges from the obtained navigation path and provides a safer and more effective evacuation route than traditional algorithms.
Highlights
Ongoing urbanization has led to an increasing number of people working and living in large buildings [1]
Indoor routing is critical for indoor fire emergency evacuation
We model and quantify the semantics of fire environments, such as path accessibility, path recognition degree, and fire parameters, and integrate them into the indoor routing method
Summary
Ongoing urbanization has led to an increasing number of people working and living in large buildings [1]. Such environments are crowded, and a high number of casualties and property damage often occurs in the case of an emergency, such as a fire. Fire-oriented indoor routing finds an optimal path based on an indoor model of a building in a fire scenario. We model and quantify the semantics of fire environments, such as path accessibility, path recognition degree, and fire parameters, and integrate them into the indoor routing method.
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