The availability of accurate localization is critical for multi-robot
exploration strategies; noisy or inconsistent localization causes failure in
meeting exploration objectives. We aim to achieve high localization accuracy
with contemporary exploration map belief and vice versa without needing global
localization information. This paper proposes a novel simultaneous exploration
and localization (SEAL) approach, which uses Gaussian Processes (GP)-based
information fusion for maximum exploration while performing communication graph
optimization for relative localization. Both these cross-dependent objectives
were integrated through the Rao-Blackwellization technique. Distributed
linearized convex hull optimization is used to select the next-best unexplored
region for distributed exploration. SEAL outperformed cutting-edge methods on
exploration and localization performance in extensive ROS-Gazebo simulations,
illustrating the practicality of the approach in real-world applications.

本文提出了一种名为 SEAL 的方法，该方法使用基于高斯过程的信息融合实现最大化探索，同时进行通信图优化以实现相对定位，通过 Rao-Blackwellization 技术将这两个交叉依赖的目标进行整合，并使用分布式线性化凸壳优化来选择下一个最佳未开发的区域。在广泛的 ROS-Gazebo 模拟中，SEAL 在探索和定位性能上优于先进方法，说明了该方法在实际应用中的实用性。

多机器人系统中的同时探测和定位

SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems

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.

本文提出了一种名为 EaRLaP 的语义对象映射技术，在多机器人探索中，在含有不确定性、感知降级的环境下实现高召回率和高精度，取得了成功的应用并在不同数据集上进行了表现。