FRAME: 自律地図統合のためのモジュラーフレームワーク：フィールドにおける進歩
FRAME: A Modular Framework for Autonomous Map Merging: Advancements in the Field
自己中心的なマルチロボット探索において、場所認識と学習済み記述子を用いて3D点群地図の重複を効率的に検出し、GICPで位置合わせを精緻化する地図統合フレームワークを提案し、地下環境での実証実験で有効性を示した。
著者: Nikolaos Stathoulopoulos, Björn Lindqvist, Anton Koval, Ali-akbar Agha-mohammadi, George Nikolakopoulos
分類: cs.RO, cs.CV
原文アブストラクト
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.
