  Many applications require accurate indoor localization. Fingerprint-based
localization methods propose a solution to this problem, but rely on a radio
map that is effort-intensive to acquire. We automate the radio map acquisition
phase using a software-defined radio (SDR) and a wheeled robot. Furthermore, we
open-source a radio map acquired with our automated tool for a 3GPP Long-Term
Evolution (LTE) wireless link. To the best of our knowledge, this is the first
publicly available radio map containing channel state information (CSI).
Finally, we describe first localization experiments on this radio map using a
convolutional neural network to regress for location coordinates.
