FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration

• URL: http://arxiv.org/abs/2301.09213v2
• Date: Tue, 24 Jan 2023 08:52:34 GMT
• Title: FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration
• Authors: Nikolaos Stathoulopoulos, Anton Koval, Ali-akbar Agha-mohammadi and George Nikolakopoulos
• Abstract summary: This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration. The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework's main pipeline, offer a fast and robust region overlap estimation. The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments.
• Score: 2.433860819518925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots' poses. The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework's main pipeline, offer a fast and robust region overlap estimation, hence eliminating the need for the time-consuming global feature extraction and feature matching process that is typically used in 3D map integration. The region overlap estimation provides a homogeneous rigid transform that is applied as an initial condition in the point cloud registration algorithm Fast-GICP, which provides the final and refined alignment. The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments, where both ground and aerial robots are deployed, with different sensor configurations.

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