Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender systems.The existing floor localization systems have many drawbacks,like low accuracy,poo...Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender systems.The existing floor localization systems have many drawbacks,like low accuracy,poor scalability,and high computational costs.In this paper,we first frame the problem of floor localization as one of learning node embeddings to predict the floor label of a subgraph.Then,we introduce FloorLocator,a deep learning-based method for floor localization that integrates efficient spiking neural networks with powerful graph neural networks.This approach offers high accuracy,easy scalability to new buildings,and computational efficiency.Experimental results on using several public datasets demonstrate that FloorLocator outperforms state-of-the-art methods.Notably,in building B0,FloorLocator achieved recognition accuracy of 95.9%,exceeding state-of-the-art methods by at least 10%.In building B1,it reached an accuracy of 82.1%,surpassing the latest methods by at least 4%.These results indicate FloorLocator’s superiority in multi-floor building environment localization.展开更多
基金supported by the National Natural Science Foundation of China(No.42174050,62172066,62172064,62322601)National Science Foundation for Excellent Young Scholars(No.62322601)+5 种基金Open Research Projects of Zhejiang Lab(No.K2022NB0AB07)Venture&Innovation Support Program for Chongqing Overseas Returnees(No.cx2021047)Chongqing Startup Project for Doctorate Scholars(No.CSTB2022BSXM-JSX005)Excellent Youth Foundation of Chongqing(No.CSTB2023NSCQJQX0025)China Postdoctoral Science Foundation(No.2023M740402)Fundamental Research Funds for the Central Universities(No.2023CDJXY-038,2023CDJXY-039).
文摘Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender systems.The existing floor localization systems have many drawbacks,like low accuracy,poor scalability,and high computational costs.In this paper,we first frame the problem of floor localization as one of learning node embeddings to predict the floor label of a subgraph.Then,we introduce FloorLocator,a deep learning-based method for floor localization that integrates efficient spiking neural networks with powerful graph neural networks.This approach offers high accuracy,easy scalability to new buildings,and computational efficiency.Experimental results on using several public datasets demonstrate that FloorLocator outperforms state-of-the-art methods.Notably,in building B0,FloorLocator achieved recognition accuracy of 95.9%,exceeding state-of-the-art methods by at least 10%.In building B1,it reached an accuracy of 82.1%,surpassing the latest methods by at least 4%.These results indicate FloorLocator’s superiority in multi-floor building environment localization.
