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FloorSense: a novel crowdsourcing map construction algorithm based on conditional random field

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Abstract

Indoor map construction by crowdsourcing has been an attractive topic. Previous works mostly focused on the indoor layout, which could be regarded as a grammar map. In this paper, we utilize CRF (conditional random field) to generate a semantic map in a mall scenario, herein predicting the function of each compartment in the grammar map. The semantic map construction consists of three steps. First, individual user’s activity patterns are featured by collecting and analyzing sensor data of smartphones. Second, the indoor layout is decided by crowdsourcing users’ uploading tracks that are obtained from the activity patterns. Third, the function of each compartment is inferred by CRF on basis of the previous two-step output. Experiments verified the prediction effectiveness and presented the prediction accuracy. This research discovers a possibility of semantic map construction without intensive labor.

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Acknowledgments

The authors acknowledge support from China Scholarship Council.

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Authors and Affiliations

  1. Beijing Key Laboratory of Network System and Network Culture, Beijing University of Posts and Telecommunications, Beijing, China

    Zhuqing Jiang

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Jiahao Zhang

  3. China Academy of Space Technology, Beijing, China

    Chonghua Liu

  4. Agricultural Bank of China, Beijing, China

    Chengkai Huang

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  1. Zhuqing Jiang
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  2. Jiahao Zhang
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  3. Chonghua Liu
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  4. Chengkai Huang
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Correspondence to Zhuqing Jiang.

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Jiang, Z., Zhang, J., Liu, C. et al. FloorSense: a novel crowdsourcing map construction algorithm based on conditional random field. Pers Ubiquit Comput 24, 499–510 (2020). https://doi.org/10.1007/s00779-019-01229-w

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  • DOI: https://doi.org/10.1007/s00779-019-01229-w

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