论文标题
早期召回，晚期精度：在感知衰减的环境中操作约束下的多机器人语义对象映射
Early Recall, Late Precision: Multi-Robot Semantic Object Mapping under Operational Constraints in Perceptually-Degraded Environments
论文作者
论文摘要
在远程多机器人自主探索任务（例如搜索和响应）期间，在不确定的，感知下降的环境中映射语义对象映射非常重要且具有挑战性。在此类任务期间，需要高度召回，避免缺少真正的目标对象，而高精度对于避免在假阳性上浪费宝贵的操作时间至关重要。鉴于视觉感知算法的最新进展，前者在很大程度上是可以自主解决的，但是如果没有人类操作员的监督，后者很难解决。但是，除非经过适当管理，否则操作约束，例如任务时间，计算要求，网络网络带宽等，除非经过适当管理，否则可以使操作员的任务变得不可行。我们提出了早期的召回，晚期精度（Earlap）语义对象映射管道，以解决此问题。 Earlap在DARPA Subterranean Challenge中被Team Costar使用，在那里成功发现了机器人团队遇到的所有工件。我们将在各种数据集上讨论Earlap的这些结果和性能。
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.
