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Predicting GNSS satellite visibility from dense point clouds

EasyChair Preprint no. 1341

14 pagesDate: July 29, 2019


To help future mobile agents plan their movement in harsh environments,a predictive model has been designed to determine what areas would be favorablefor Global Navigation Satellite System (GNSS) positioning. The model is able topredict the number of viable satellites for a GNSS receiver, based on a 3D pointcloud map and a satellite constellation. Both occlusion and absorption effects ofthe environment are considered. A rugged mobile platform was designed to collectdata in order to generate the point cloud maps. It was deployed during the Canadianwinter known for large amounts of snow and extremely low temperatures. The testenvironments include a highly dense boreal forest and a university campus compris-ing high buildings. The experiment results indicate that the model performs well inboth structured and unstructured environments.

Keyphrases: DGNSS, GNSS, GPS, LiDAR, Localization, Mapping, RTK, Winter

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Philippe Dandurand and Philippe Babin and Vladimír Kubelka and Philippe Giguère and François Pomerleau},
  title = {Predicting GNSS satellite visibility from dense point clouds},
  howpublished = {EasyChair Preprint no. 1341},

  year = {EasyChair, 2019}}
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