  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 critical to avoid wasting valuable operational time on false positives.
Given recent advancements in visual perception algorithms, the former is
largely solvable autonomously, but the latter is difficult to address without
the supervision of a human operator. However, operational constraints such as
mission time, computational requirements, mesh network bandwidth and so on, can
make the operator's task infeasible unless properly managed. We propose the
Early Recall, Late Precision (EaRLaP) semantic object mapping pipeline to solve
this problem. EaRLaP was used by Team CoSTAR in DARPA Subterranean Challenge,
where it successfully detected all the artifacts encountered by the team of
robots. We will discuss these results and performance of the EaRLaP on various
datasets.
