FRAME: A Modular Framework for Autonomous Map-merging: Advancements in the Field
arxiv(2024)
摘要
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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