Radio propagation models for differential GNSS based on dense point clouds
Abstract 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 geolocati...
| Published in: | Journal of Field Robotics |
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| Main Authors: | , , , , |
| Other Authors: | |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2020
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1002/rob.21988 https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.21988 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/rob.21988 |
| Summary: | Abstract 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 forest, it becomes critical to predict how the environment will impact the propagation of these radio signals. To this effect, we present a new 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. Our model can predict both the number of usable satellites for a GNSS receiver and the strength of the reference radio signal used in the differential GNSS scenario. Contrary to others, it takes into account both signal occlusion and absorption mechanisms, given the geometry and density of the point cloud map. We designed two rugged mobile data‐collecting platforms, both to generate the 3D maps of the environment, as well as to gather various ground truth for GNSS satellite and local radio signals. Environments used for our field deployments included a boreal forest, a subarctic forest and diverse industrial areas. Experimental results indicate that our model performs well in both structured and unstructured environments, with median errors of 1.10 for the predicted number of satellites and for the strength of the differential GNSS correction signals. |
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