FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration
| Autor: | Stathoulopoulos, Nikolaos, Koval, Anton, Agha-mohammadi, Ali-akbar, Nikolakopoulos, George |
|---|---|
| Rok vydání: | 2023 |
| Předmět: | |
| DOI: | 10.48550/arxiv.2301.09213 |
| Popis: | 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. Comment: to be published |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|