Get to the Point: Learning Lidar Place Recognition and Metric Localisation Using Overhead Imagery

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Abstract

This paper is about localising a robot in overhead images using lidar. Specifically; we show how to solve both place recognition and metric localisation of a lidar using only publicly available overhead imagery as a map proxy. This is in contrast to current approaches that rely on prior sensor maps. To handle the drastic modality difference (overhead image vs. on the ground lidar); our method learns a representation that purposely and suitably transforms a given overhead image into a collection of 2D points; allowing for direct comparison against lidar scans. After both modalities are expressed as points; point-based methods can then be leveraged to learn the registration and place recognition task. Our method is the first to learn the place recognition of a lidar using only overhead imagery; and outperforms prior work for metric localisation with large initial pose offsets.

Paper: https://doi.org/10.15607/rss.2021.xvii.003

Publication record: https://www.roboticsproceedings.org/rss17/p003.html

Bibtex

@inproceedings{gettothepoint2021,
  author={Tang, Tim and De Martini, Daniele and Newman, Paul},
  title={Get to the Point: Learning Lidar Place Recognition and Metric Localisation Using Overhead Imagery},
  year={2021},
  booktitle={Robotics: Science and Systems XVII},
  doi={10.15607/rss.2021.xvii.003},
  url={https://doi.org/10.15607/rss.2021.xvii.003},
}