Guobin Shen, Zhuo Chen, Peichao Zhang, Thomas Moscibroda, and Yongguang Zhang, Microsoft Research Asia
Abstract:
We present Walkie-Markie — an indoor pathway mapping system that can automatically reconstruct internal pathway maps of buildings without any a-priori knowledge about the building, such as the floor plan or access point locations. Central to Walkie-Markie is a novel exploitation of the WiFi infrastructure to define landmarks (WiFi-Marks) to fuse crowdsourced user trajectories obtained from inertial sensors on users’ mobile phones. WiFi-Marks are special pathway locations at which the trend of the received WiFi signal strength changes from increasing to decreasing when moving along the pathway. By embedding these WiFi-Marks in a 2D plane using a newly devised algorithm and connecting them with calibrated user trajectories, Walkie-Markie is able to infer pathway maps with high accuracy. Our experiments demonstrate that Walkie-Markie is able to reconstruct a high-quality pathway map for a real office-building floor after only 5-6 rounds of walks, with accuracy gradually improving as more user data becomes available.The maximum discrepancy between the inferred pathway map and the real one is within 3m and 2.8m for the anchor nodes and path segments, respectively.
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BibTeX
@inproceedings {180297,
author = {Guobin Shen and Zhuo Chen and Peichao Zhang and Thomas Moscibroda and Yongguang Zhang},
title = {{Walkie-Markie}: Indoor Pathway Mapping Made Easy},
booktitle = {10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13)},
year = {2013},
isbn = {978-1-931971-00-3},
address = {Lombard, IL},
pages = {85--98},
url = {https://www.usenix.org/conference/nsdi13/technical-sessions/presentation/shen},
publisher = {USENIX Association},
month = apr
}
In-building localization continues to be an important problem in our increasingly mobile world. One of the important requirements of in-building localization is the availability of a map for indoor pathways. The Walkie-Markie paper presents a technique that tries to automatically determine the indoor pathway map of a building, by utilizing crowd-sourced data from WiFi devices.
Combining data from multi-modal sensors for more accurate localization and trajectory inferencing is an increasingly popular approach. Many localization techniques have used that approach in recent years, and this paper leverages similar information for determining popular and likely pathways in a building by fusing human trajectories. The techniques are grounded in a nice theoretical framework. The concept of WiFiMarks, which identify inflection points in various WiFi signal strengths, seem intuitive and useful.
The Walkie-Markie technique, clearly, is one part of the bigger problem in automatically determining indoor maps. Pathways are only part of a map in a building; one needs to infer other kinds of spaces to complete this problem and perhaps there will other techniques, some possibly combined with manual intervention that will make this system really practical and usable. In summary, this adds a some new techniques and ideas to a growing body of literature in this broad domain.
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