Fast fingerprint database maintenance for indoor positioning based on UGV SLAM.
Indoor positioning technology has become more and more important in the last two decades. Utilizing Received Signal Strength Indicator (RSSI) fingerprints of Signals of OPportunity (SOP) is a promising alternative navigation solution. However, as the RSSIs vary during operation due to their physical nature and are easily affected by the environmental change, one challenge of the indoor fingerprinting method is maintaining the RSSI fingerprint database in a timely and effective manner. In this paper, a solution for rapidly updating the fingerprint database is presented, based on a self-developed Unmanned Ground Vehicles (UGV) platform NAVIS. Several SOP sensors were installed on NAVIS for collecting indoor fingerprint information, including a digital compass collecting magnetic field intensity, a light sensor collecting light intensity, and a smartphone which collects the access point number and RSSIs of the pre-installed WiFi network. The NAVIS platform generates a map of the indoor environment and collects the SOPs during processing of the mapping, and then the SOP fingerprint database is interpolated and updated in real time. Field tests were carried out to evaluate the effectiveness and efficiency of the proposed method. The results showed that the fingerprint databases can be quickly created and updated with a higher sampling frequency (5Hz) and denser reference points compared with traditional methods, and the indoor map can be generated without prior information. Moreover, environmental changes could also be detected quickly for fingerprint indoor positioning.
GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430000, China. tangjian@whu.edu.cn.|Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Geodeetinrinne 2, Kirkkonummi FI-02431, Finland. tangjian@whu.edu.cn.
Jian
J
Tang
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Geodeetinrinne 2, Kirkkonummi FI-02431, Finland. yuwei.chen@nls.fi.
Yuwei
Y
Chen
Department of Navigation and Positioning, Finnish Geospatial Research Institute, Geodeetinrine 2, Kirkkonummi FI-02431, Finland. liang.chen@nls.fi.
Liang
L
Chen
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Geodeetinrinne 2, Kirkkonummi FI-02431, Finland. jingbin.liu@nls.fi.
Jingbin
J
Liu
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Geodeetinrinne 2, Kirkkonummi FI-02431, Finland. juha.hyyppa@nls.fi.
Juha
J
Hyyppä
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Geodeetinrinne 2, Kirkkonummi FI-02431, Finland. antero.kukko@nls.fi.
Antero
A
Kukko
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, Geodeetinrinne 2, Kirkkonummi FI-02431, Finland. harri.kaartinen@nls.fi.
Harri
H
Kaartinen
Department of Real Estate, Planning and Geoinformatics, Aalto University, P.O. Box 11000, Espoo FI-00076, Finland. hannu.hyyppa@aalto.fi.
Hannu
H
Hyyppä
Conrad Blucher Institute of Surveying & Science, Texas A&M University at Corpus Christi, Corpus Christi, TX 77843-3577, USA. ruizhi.chen@tamucc.edu.
Ruizhi
R
Chen