Sustainable Energy for Smart Cities. First EAI International Conference, SESC 2019, Braga, Portugal, December 4–6, 2019, Proceedings

Research Article

Acoustic Simultaneous Localization and Mapping Using a Sensor-Rich Smartphone

  • @INPROCEEDINGS{10.1007/978-3-030-45694-8_19,
        author={Xi Song and Mei Wang and Hong-Bing Qiu and Xueming Wei},
        title={Acoustic Simultaneous Localization and Mapping Using a Sensor-Rich Smartphone},
        proceedings={Sustainable Energy for Smart Cities. First EAI International Conference, SESC 2019, Braga, Portugal, December 4--6, 2019, Proceedings},
        proceedings_a={SESC},
        year={2020},
        month={6},
        keywords={Room geometry reconstruction Smartphone-based self-positioning Simultaneous localization and mapping},
        doi={10.1007/978-3-030-45694-8_19}
    }
    
  • Xi Song
    Mei Wang
    Hong-Bing Qiu
    Xueming Wei
    Year: 2020
    Acoustic Simultaneous Localization and Mapping Using a Sensor-Rich Smartphone
    SESC
    Springer
    DOI: 10.1007/978-3-030-45694-8_19
Xi Song1,*, Mei Wang2, Hong-Bing Qiu1,*, Xueming Wei1,*
  • 1: Guilin University of Electronic Technology
  • 2: Guilin University of Technology
*Contact email: songxiyu@guet.edu.cn, qiuhb@guet.edu.cn, 31696712@qq.com

Abstract

The problem of simultaneous localization and mapping (SLAM) has been extensively studied by using a variety of specialized sensors. In this paper, we show that the SLAM could be realized using a sensor-rich smartphone. We assume that an indoor pedestrian always carries a sounding smartphone and the pedestrian moves autonomously inside a room. At every step, the loudspeaker of the smartphone produces a chirp pulse (frequency band is in the upper of human hearing area), the microphone of this smartphone registers the echoes, and the inertial sensors record the accelerometer and gyroscope readings, then the position of the moving pedestrian and the geometry map of the room are done simultaneously. However, when in a rectangular room of regular shape, reconstructing the room geometry at each sound source position is quite redundant. To avoid this redundancy and improve the sound source localization performance, we address SLAM by Matrix Analysis-based geometry estimation, and then this information is applied to the real-time positioning requirements taking the advantage of multi-source information fusion concept. Finally, we show the effectiveness of the proposed SLAM method by experiments with real measured acoustic events, the result fully demonstrates that the proposed SLAM method could be easily implemented using the smartphone carried by the pedestrian.