Robotics: Science and Systems XIX
Self-Supervised Lidar Place Recognition in Overhead Imagery Using Unpaired Data
Tim Y. Tang, Daniele De Martini, Paul M NewmanAbstract:
As much as place recognition is crucial for navigation, mapping and collecting training ground truth, namely sensor data pairs across different locations, are costly and time-consuming. This paper tackles these by learning lidar place recognition on public overhead imagery and in a self-supervised fashion, with no need for paired lidar and overhead imagery data. We learn the cross-modal data comparison between lidar and overhead imagery with a multi-step framework. First, images are transformed into synthetic lidar data and a latent projection is learned. Next, we discover pseudo pairs of lidar and satellite data from unpaired and asynchronous sequences, and use them for training a final embedding space projection in a cross-modality place recognition framework. We train and test our approach on real data from various environments and show performances approaching a supervised method using paired data.
Bibtex:
@INPROCEEDINGS{Tang-RSS-23, AUTHOR = {Tim Y. Tang AND Daniele De Martini AND Paul M Newman}, TITLE = {{Self-Supervised Lidar Place Recognition in Overhead Imagery Using Unpaired Data}}, BOOKTITLE = {Proceedings of Robotics: Science and Systems}, YEAR = {2023}, ADDRESS = {Daegu, Republic of Korea}, MONTH = {July}, DOI = {10.15607/RSS.2023.XIX.098} }