@inproceedings{7b0f35821e8e4247a89a8420f86c4351,
title = "WalkSLAM: A Walking Pattern-Based Mobile SLAM Solution",
abstract = "In indoor localization scenarios, a sheer coordinate with respect to a basis is insufficient to indicate the users{\textquoteright} situation due to a lack of information about landmarks distributed in the environments. To extract landmarks{\textquoteright} information manually, however, is inefficient and thus vulnerable to changes of the environments. Simultaneous localization and mapping can solve the localization and landmarks{\textquoteright} information extracting problems. This paper presents WalkSLAM, a SLAM solution that estimates both the path taken by the user and the locations of Wi-fi devices in the indoor space, using a smartphone. This solution extends the previous work by introducing human walking patterns into the specific SLAM problem. Experiments demonstrate that the improvement consists of increased efficiency of the particle filter, and hence, of the overall algorithm, and a better estimation of the user{\textquoteright}s location and path.",
keywords = "Indoor localization, Simultaneous localization and mapping, Walk ratio, Walking pattern",
author = "Lin Ma and Tianyang Fang and Danyang Qin",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; International Conference on Communications, Signal Processing, and Systems, CSPS 2018 ; Conference date: 14-07-2018 Through 16-07-2018",
year = "2020",
doi = "10.1007/978-981-13-6504-1\_160",
language = "英语",
isbn = "9789811365034",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "1347--1354",
editor = "Qilian Liang and Xin Liu and Zhenyu Na and Wei Wang and Jiasong Mu and Baoju Zhang",
booktitle = "Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II",
address = "德国",
}