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

• URL: http://arxiv.org/abs/2404.18006v2
• Date: Wed, 28 Aug 2024 12:43:26 GMT
• Title: FRAME: A Modular Framework for Autonomous Map Merging: Advancements in the Field
• Authors: Nikolaos Stathoulopoulos, Björn Lindqvist, Anton Koval, Ali-akbar Agha-mohammadi, George Nikolakopoulos,
• Abstract summary: This article presents a novel approach for merging 3D point cloud maps in the context of egocentric multi-robot exploration. The proposed approach leverages state-of-the-art place recognition and learned descriptors to efficiently detect overlap between maps. The effectiveness of the proposed framework is successfully demonstrated through multiple field missions of robot exploration.
• Score: 12.247977717070773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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