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

TL;DR

The paper introduces EaRLaP, a semantic object mapping pipeline designed for multi-robot exploration in degraded environments, balancing early recall and late precision to optimize detection and operational efficiency.

Contribution

The paper presents the novel EaRLaP pipeline that manages the trade-off between recall and precision in multi-robot semantic mapping under operational constraints.

Findings

EaRLaP successfully detected all artifacts in DARPA Subterranean Challenge.

EaRLaP performs well across various datasets.

The approach improves operational efficiency in challenging environments.

Abstract

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.