In this article, a novel approach for merging 3D point cloud maps in the
context of egocentric multi-robot exploration is presented. Unlike traditional
methods, the proposed approach leverages state-of-the-art place recognition and
learned descriptors to efficiently detect overlap between maps, eliminating the
need for the time-consuming global feature extraction and feature matching
process. The estimated overlapping regions are used to calculate a homogeneous
rigid transform, which serves as an initial condition for the GICP point cloud
registration algorithm to refine the alignment between the maps. The advantages
of this approach include faster processing time, improved accuracy, and
increased robustness in challenging environments. Furthermore, the
effectiveness of the proposed framework is successfully demonstrated through
multiple field missions of robot exploration in a variety of different
underground environments.

在本研究中，提出了一种新颖的方法，用于在自我中心多机器人探索的背景下合并 3D 点云地图。与传统方法不同，该方法利用先进的地点识别和学习描述符来高效地检测地图之间的重叠，消除了耗时的全局特征提取和特征匹配过程。估计的重叠区域用于计算同一刚体变换，作为 GICP 点云配准算法的初始条件，进一步改善地图之间的对齐。该方法的优势包括更快的处理时间、提高的准确性和对挑战环境的增强鲁棒性。此外，通过多个机器人在各种不同地下环境中的实地任务成功证明了该框架的有效性。

FRAME：自主地图合并的模块化框架：领域进展

FRAME: A Modular Framework for Autonomous Map-merging: Advancements in  the Field

The monocular visual-inertial system (VINS), which consists one camera and
one low-cost inertial measurement unit (IMU), is a popular approach to achieve
accurate 6-DOF state estimation. However, such locally accurate visual-inertial
odometry is prone to drift and cannot provide absolute pose estimation.
Leveraging history information to relocalize and correct drift has become a hot
topic. In this paper, we propose a monocular visual-inertial SLAM system, which
can relocalize camera and get the absolute pose in a previous-built map. Then
4-DOF pose graph optimization is performed to correct drifts and achieve global
consistent. The 4-DOF contains x, y, z, and yaw angle, which is the actual
drifted direction in the visual-inertial system. Furthermore, the proposed
system can reuse a map by saving and loading it in an efficient way. Current
map and previous map can be merged together by the global pose graph
optimization. We validate the accuracy of our system on public datasets and
compare against other state-of-the-art algorithms. We also evaluate the map
merging ability of our system in the large-scale outdoor environment. The
source code of map reuse is integrated into our public code, VINS-Mono.

本文提出了一种单目视觉惯性 SLAM 系统，可以通过重新定位相机和获取先前构建的地图中的绝对姿态来校正漂移，并进行全局一致性的 4 自由度姿态图优化，进而实现地图合并。