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.

翻译：在诸如搜索和救援等远程多机器人自主勘探任务期间,在不确定、感知退化的环境中进行语义物体绘图十分重要和富有挑战性。在这类任务中,高提醒对于避免丢失真实目标物体是可取的,高精确性对于避免将宝贵的操作时间浪费在假阳性上也至关重要。鉴于视觉认知算法最近的进展,前者基本上可以自主解决,但在没有人类操作者监督的情况下,后者很难解决。然而,任务时间、计算要求、网格网络带宽等操作限制使操作者的任务不可行,除非管理得当。我们提出了早期召回、延迟精度(EaRLAP) 的语义物体绘图管道来解决这一问题。EaRLAP是DARPA Subterranean Challenge的COSTAR小组在DARPARA Subterrane Challenge中成功探测到机器人团队遇到的所有艺术品。我们将讨论这些结果和EaRLAP在不同数据集上的性能。