Predicting GNSS satellite visibility from dense point clouds
This work addresses the challenge of reliable GNSS positioning for mobile agents in difficult conditions like dense forests and urban areas, though it appears incremental as it builds on existing point cloud and satellite modeling techniques.
The researchers tackled the problem of predicting GNSS satellite visibility for mobile agents in harsh environments by developing a model that uses 3D point clouds and satellite constellations, achieving good performance in both structured and unstructured test environments.
To help future mobile agents plan their movement in harsh environments,a predictive model has been designed to determine what areas would be favorable for Global Navigation Satellite System (GNSS) positioning. The model is able to predict the number of viable satellites for a GNSS receiver, based on a 3D point cloud map and a satellite constellation. Both occlusion and absorption effects of the environment are considered. A rugged mobile platform was designed to collect data in order to generate the point cloud maps. It was deployed during the Canadian winter known for large amounts of snow and extremely low temperatures. The test environments include a highly dense boreal forest and a university campus with high buildings. The experiment results indicate that the model performs well in both structured and unstructured environments