  Radio Environmental Maps (REMs) are a powerful tool for enhancing the
performance of various communication and networked agents. However, generating
REMs is a laborious undertaking, especially in complex 3-Dimensional (3D)
environments, such as indoors. To address this issue, we propose a system for
autonomous generation of fine-grained REMs of indoor 3D spaces. In the system,
multiple small indoor Unmanned Aerial Vehicles (UAVs) are sequentially used for
3D sampling of signal quality indicators. The collected readings are
streamlined to a Machine Learning (ML) system for its training and, once
trained, the system is able to predict the signal quality at unknown 3D
locations. The system enables automated and autonomous REM generation, and can
be straightforwardly deployed in new environments. In addition, the system
supports REM sampling without self-interference and is technology-agnostic, as
long as the REM-sampling receivers features suitable sizes and weights to be
carried by the UAVs. In the demonstration, we instantiate the system design
using two UAVs and show its capability of visiting 72 waypoints and gathering
thousands of Wi-Fi data samples. Our results also include an instantiation of
the ML system for predicting the Received Signal Strength (RSS) of known Wi-Fi
Access Points (APs) at locations not visited by the UAVs.
