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.
Abstract (translated)
本文提出了一种基于重叠检测和对齐的 egocentric 异质多机器人探索框架,该框架无需手动初始猜测或机器人姿态的先前知识。该新提出的解决方案利用先进的地点识别学习特征描述器,通过框架的主要通道提供快速且可靠的区域重叠估计,从而消除了通常用于 3D 地图集成的费时的全球特征提取和特征匹配过程。区域重叠估计提供一种均匀的 rigid 变换,将其作为点云注册算法 Fast-GICP 的初始条件,提供最终的 refined 对齐。该框架的有效性通过多个地下多机器人探索任务在不同类型的传感器配置下的实验评估。
URL
https://arxiv.org/abs/2301.09213
