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 Newman

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.

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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} 
}