Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings

@article{MunozBanon2022RobustSD,
  title={Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings},
  author={Miguel 'Angel Munoz-Ban'on and Jan-Hendrik Pauls and Haohao Hu and Christoph Stiller and Francisco A. Candelas and Fernando Torres},
  journal={IEEE Robotics and Automation Letters},
  year={2022},
  volume={7},
  pages={12339-12346}
}
Localization in aerial imagery-based maps offers many advantages, such as global consistency, geo-referenced maps, and the availability of publicly accessible data. However, the landmarks that can be observed from both aerial imagery and on-board sensors is limited. This leads to ambiguities or aliasing during the data association. Building upon a highly informative representation (that allows efficient data association), this paper presents a complete pipeline for resolving these ambiguities… 

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