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tors, called LF, is the encoder output whilst the other,
called LGID, is the decoder output. Both descriptors
can be compared among different images to decide
if they are likely to close a loop. The experimental
results show the ability of the presented approach to
detect loop candidates and compare favourably to a
previously existing method.
Since our proposal has shown to be trainable with
a small set of images and the architecture is simple
enough to provide on-line sets of loop candidates,
it constitutes a promising approach to be embedded
into a full underwater visual SLAM system. Even
though it cannot replace a full SLAM loop closing
layer, since it does not compute the relative motion
between images, it can strongly reduce the compu-
tational load of such module by feeding it only with
images that most likely will close a loop.
Accordingly, our lines of future research are as
follows. First, we are now working on a strategy
to confirm or deny the loop candidates as well as to
compute the relative motion between the confirmed
loops. In this way, our proposal could be embedded
into a full SLAM system. Second, even though small
datasets have shown to be sufficient to reach good
results, larger datasets would probably lead to better
candidate sets. For this reason, we are also working
on a method to synthetically generate loops from un-
derwater imagery. This would constitute a weakly su-
pervised approach and would allow training the NN
with arbitrarily large datasets. Our final goal would
be to integrate the whole loop detection not only into
a SLAM system but also into a Multi-Session SLAM
system, which will definitely prove the ability of our
proposal to detect loops in a really challenging sce-
nario.
ACKNOWLEDGEMENTS
This work is partially supported by the Spanish Min-
istry of Economy and Competitiveness under contract
DPI2017-86372-C3-3-R (AEI,FEDER,UE).
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