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FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration
- Publication Year :
- 2023
-
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.<br />Comment: to be published
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2301.09213
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/ICRA48891.2023.10160771