Accurate geolocation of mobile equipment operating in outdoor environments is an increasingly important question in robotics and automation. Modern geolocation systems, however, rely on the crucial ability for a mobile device to receive specific radio signals at all times. As such geolocation systems are increasingly deployed in harsh or difficult environments, for example, in the presence of tall buildings or dense forests, it becomes critical to predict how the environment will impact the propagation of these radio signals. We propose a signal propagation model that can determine what areas would be favorable for global navigation satellite system (GNSS) positioning, based on a prior three‐dimensional (3D) point cloud map of the environment. The model predicts both the number of usable satellites for a GNSS receiver and the strength of the reference radio signal used in the differential GNSS scenario. We take into account both signal occlusion and absorption mechanisms, given the geometry and density of the point cloud map. The design of the model is data-driven, based on experiments performed both at the university campus and in the Montmorency forest.

The presentation will be given in English and the slides will be in English.

Zoom Meeting
To obtain the Zoom meeting web link, please contact:
Annette.Schwerdtfeger@gel.ulaval.ca