An Algorithmic Approach to Quantifying GPS Trajectory Error

Abstract

The alignment of aerial and satellite imagery with ground sensor data is an ongoing research challenge. In dense urban environments, part of this challenge is induced by the positioning error of Global Positioning System (GPS). Despite the potential for error, many studies use GPS in order to infer road networks because GPS data is inexpensive and can be acquired quickly. Major transit organizations are freely providing data on the real-time position of their buses as well as ground truth route trajectories. This work exploits geospatial open data to construct a database of historical GPS from bus roads. Using this database, the GPS error map along main arteries of major cities can be reconstructed. The extraction of error maps is highly relevant for the planning and the joint exploitation of airborne and ground-based imagery. In this work, we use bus routes in downtown Victoria, BC, Canada and Adelaide, Australia to demonstrate the extraction GPS error maps.

Cite

Text

Plaudis et al. "An Algorithmic Approach to Quantifying GPS Trajectory Error." IEEE/CVF International Conference on Computer Vision Workshops, 2021. doi:10.1109/ICCVW54120.2021.00437

Markdown

[Plaudis et al. "An Algorithmic Approach to Quantifying GPS Trajectory Error." IEEE/CVF International Conference on Computer Vision Workshops, 2021.](https://mlanthology.org/iccvw/2021/plaudis2021iccvw-algorithmic/) doi:10.1109/ICCVW54120.2021.00437

BibTeX

@inproceedings{plaudis2021iccvw-algorithmic,
  title     = {{An Algorithmic Approach to Quantifying GPS Trajectory Error}},
  author    = {Plaudis, Matthew and Azam, Muhammad and Jacoby, Derek and Drouin, Marc-Antoine and Coady, Yvonne},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2021},
  pages     = {3902-3909},
  doi       = {10.1109/ICCVW54120.2021.00437},
  url       = {https://mlanthology.org/iccvw/2021/plaudis2021iccvw-algorithmic/}
}