Smartphone based indoor localization has been widely explored for meeting the demand of high-precision low-cost indoor localization. Previous methods focus mainly on improving the localization accuracy of single sensor based localization, which may hinder their applications. In this article, we propose a novel encoder-decoder architecture for high-precision low-cost indoor localization. We first leverage two modal-specific encoders for feature extraction. Then, we propose two feature-level fusion strategies for feature fusion. Finally, we leverage two task-specific decoders for both position and orientation prediction. During training, we adopt WiFi-aided learning to provide a more reliable label. We test the proposed method in the corridor environment of a typical building. Experiments results show that our method can achieve less than a half meter localization accuracy, and meanwhile enjoys the run-time efficiency.


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    Title :

    Hybrid Cascaded and Feature-Level Fusion Scheme for Multi-Modal Indoor Localization


    Contributors:


    Publication date :

    2023-06-01


    Size :

    1063448 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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