  Simultaneous localization and mapping (SLAM) frameworks for autonomous
navigation rely on robust data association to identify loop closures for
back-end trajectory optimization. In the case of autonomous underwater vehicles
(AUVs) equipped with multibeam echosounders (MBES), data association is
particularly challenging due to the scarcity of identifiable landmarks in the
seabed, the large drift in dead-reckoning navigation estimates to which AUVs
are prone and the low resolution characteristic of MBES data. Deep learning
solutions to loop closure detection have shown excellent performance on data
from more structured environments. However, their transfer to the seabed domain
is not immediate and efforts to port them are hindered by the lack of
bathymetric datasets. Thus, in this paper we propose a neural network
architecture aimed to showcase the potential of adapting such techniques to
correspondence matching in bathymetric data. We train our framework on real
bathymetry from an AUV mission and evaluate its performance on the tasks of
loop closure detection and coarse point cloud alignment. Finally, we show its
potential against a more traditional method and release both its implementation
and the dataset used.
