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Predicting GNSS satellite visibility from densepoint clouds

2019-04-16
Philippe Dandurand, Philippe Babin, Vladimır Kubelka, Philippe Giguère, François Pomerleau

Abstract

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 \ac{GNSS} positioning. The model is able to predict the number of viable satellites for a \ac{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.

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URL

http://arxiv.org/abs/1904.07837

PDF

http://arxiv.org/pdf/1904.07837


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