@inproceedings{d68b2f0e5b6249838a92adc72e5fc8a8,
title = "MagPIE: A dataset for indoor positioning with magnetic anomalies",
abstract = "In this paper, we present a publicly available dataset for the evaluation of indoor positioning algorithms that use magnetic anomalies. Our dataset contains IMU and magnetometer measurements along with ground truth position measurements that have centimeter-level accuracy. To produce this dataset, we collected over 13 hours of data (51 kilometers of total distance traveled) from three different buildings, with sensors both handheld and mounted on a wheeled robot, in environments with and without changes in the placement of objects that affect magnetometer measurements ({"}live loads{"}). We conclude the paper with a discussion of why these characteristics of our dataset are important when evaluating positioning algorithms.",
keywords = "Comparison of methods, Dataset, Indoor localization, Magnetic localization",
author = "David Hanley and Faustino, {Alexander B.} and Zelman, {Scott D.} and Degenhardt, {David A.} and Timothy Bretl",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2017 ; Conference date: 18-09-2017 Through 21-09-2017",
year = "2017",
month = nov,
day = "20",
doi = "10.1109/IPIN.2017.8115961",
language = "English (US)",
series = "2017 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--8",
booktitle = "2017 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2017",
address = "United States",
}