  Several SLAM methods benefit from the use of semantic information. Most
integrate photometric methods with high-level semantics such as object
detection and semantic segmentation. We propose that adding a semantic
segmentation decoder in a shared encoder architecture would help the descriptor
decoder learn semantic information, improving the feature extractor. This would
be a more robust approach than only using high-level semantic information since
it would be intrinsically learned in the descriptor and would not depend on the
final quality of the semantic prediction. To add this information, we take
advantage of multi-task learning methods to improve accuracy and balance the
performance of each task. The proposed models are evaluated according to
detection and matching metrics on the HPatches dataset. The results show that
the Semantic SuperPoint model performs better than the baseline one.
