← Search

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 5Source ↗Scholar ↗
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 21Source ↗Scholar ↗