Ambient indoor localization is a research field that studies indoor localization systems based on ambient signals of opportunity, such as those from broadcasting TV and FM radio stations or GSM networks. By using already existing high-power transmitters, such systems provide very wide coverage (in contrast to current short-range solutions). However, the need for specialized receivers and laborious data collection complicate further research in this area, despite the promising initial results.
AmbiLoc solves this problem by providing a ready-made dataset of ambient radio fingerprints. The dataset has been systematically collected in multiple testbeds, including large-scale and multi-floor buildings, over the course of one year. Using AmbiLoc, any researcher can quickly experiment with ambient indoor localization, create, evaluate and compare novel localization methods.
MagPIE is 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'').
Alcala Tutorial 2017
The Alcalá Tutorial 2017 data set is an indoor localization database to test Indoor Positioning System that rely on WLAN/WiFifingerprint. It was created during the Fingerprinting-based Indoor Positioning tutorial.
The Tampere University data set is an indoor localization database to test Indoor Positioning Systems that rely on WLAN/Wi-Fi fingerprint. It was created by E.S. Lohan and J. Talvitie to be used for testing their indoor localization approaches. In this website, the original data format has been modified to be the same than other datasets also included in the website, as for instance the UjiIndoorLoc.
The IPIN2016_Tutorial is an indoor localization database to test Indoor Positioning System that rely on WLAN/WiFifingerprint. It was created during the Fingerprinting-based Indoor Positioning tutorial of the seventh international conference on indoor Positioning and Indoor Navigation (IPIN2016).
This data set is focused on WLAN fingerprint positioning technologies and methodologies (also know as WiFi Fingerprinting). The UJIIndoorLoc database covers three buildings of Universitat Jaume I with 4 or more floors and almost 110.000m2. It can be used for classification, e.g. actual building and floor identification, or regression, e.g. actual longitude and latitude estimation. It was created in 2013 by means of more than 20 different users and 25 Android devices. The database consists of 19937 training/reference records (trainingData.csv file) and 1111 validation/test records (validationData.csv file).