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Nicolas Marchal

2 accepted papers

2022

Early Recall, Late Precision: Multi-Robot Semantic Object Mapping under Operational Constraints in Perceptually-Degraded Environments

IROS 2022poster

Semantic object mapping in uncertain, perceptually degraded environments during long-range multi-robot autonomous exploration tasks such as search-and-rescue is important and challenging. During such missions, high recall is desirable to avoid missing true target objects and high precision is also c…

Cited by 5SourceScholar
2020

Learning Densities in Feature Space for Reliable Segmentation of Indoor Scenes

RA-L 2020

Deep learning has enabled remarkable advances in scene understanding, particularly in semantic segmentation tasks. Yet, current state of the art approaches are limited to a closed set of classes, and fail when facing novel elements, also known as out of distribution (OoD) data. This is a problem as

Cited by 21SourceScholar