FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration
This work addresses the challenge of autonomous map integration for multi-robot exploration in complex settings like underground areas, representing an incremental improvement over existing methods.
The paper tackles the problem of merging 3D point cloud maps from multiple robots without prior pose knowledge, achieving fast and robust alignment using learned descriptors and Fast-GICP, with experimental validation in underground environments involving ground and aerial robots.
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.