@inproceedings{645187d0183741219fad76b61123edbc,
title = "Pedestrian walking model for floor plan building based on crowdsourcing PDR data",
abstract = "Indoor navigation has gained lots of interest in the last few years due to its broad application prospect. However, indoor floor plan for position display is not always available. In this paper, we utilize the crowdsourcing pedestrian dead reckoning (PDR) data got from the smart phone to build the indoor floor plan. According to the crowdsourcing PDR data, we propose new walking model that reflects the distribution of indoor pedestrian trajectory. This model is can well express the pedestrian walking pattern. In addition, the proposed model can also estimate the hallway width through the PDR data in hallway. According to the proposed model, we can draw the floor plan with the width of hallway. We have implemented the proposed algorithm in our lab and evaluated its performances. The simulation results showed that the proposed algorithm can efficiently generate the floor plan in the unknown environments with lower cost, which can contribute a lot for indoor navigation.",
keywords = "Floor plan, IMU, Mobile crowdsourcing, PDR",
author = "Guangda Yang and Yongliang Zhang and Lin Ma and Leqi Tang",
note = "Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018.; 3rd International Conference on Machine Learning and Intelligent Communications, MLICOM 2018 ; Conference date: 06-07-2018 Through 08-07-2018",
year = "2018",
doi = "10.1007/978-3-030-00557-3\_31",
language = "英语",
isbn = "9783030005566",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Verlag",
pages = "305--316",
editor = "Limin Meng and Yan Zhang",
booktitle = "Machine Learning and Intelligent Communications - 3rd International Conference, MLICOM 2018, Proceedings",
address = "德国",
}