# Predicting GNSS satellite visibility from dense point clouds

## TL;DR

This paper presents a predictive model that estimates GNSS satellite visibility in complex environments using 3D point clouds, aiding mobile agents in navigation planning.

## Contribution

The study introduces a novel model that accounts for occlusion and absorption effects to predict satellite visibility from dense point cloud maps.

## Key findings

- Model accurately predicts satellite visibility in diverse environments.
- Effective in both dense forests and urban areas.
- Demonstrates robustness in winter conditions with snow.

## 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 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