Robotics: Science and Systems XVII
Get to the Point: Learning Lidar Place Recognition and Metric Localisation Using Overhead Imagery
Tim Y. Tang, Daniele De Martini, Paul NewmanAbstract:
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.
Bibtex:
@INPROCEEDINGS{Tang-RSS-21, AUTHOR = {Tim Y. Tang AND Daniele {De Martini} AND Paul Newman}, TITLE = {{Get to the Point: Learning Lidar Place Recognition and Metric Localisation Using Overhead Imagery}}, BOOKTITLE = {Proceedings of Robotics: Science and Systems}, YEAR = {2021}, ADDRESS = {Virtual}, MONTH = {July}, DOI = {10.15607/RSS.2021.XVII.003} }