FRAME: A Modular Framework for Autonomous Map Merging: Advancements in the Field

TL;DR

This paper introduces FRAME, a modular framework that enhances 3D map merging for multi-robot exploration by using advanced place recognition and learned descriptors, resulting in faster, more accurate, and robust map alignment.

Contribution

The paper presents a novel, modular approach that combines place recognition and learned descriptors for efficient and robust 3D map merging in multi-robot exploration.

Findings

Faster processing time compared to traditional methods.

Improved accuracy in map alignment.

Effective in challenging underground environments.

Abstract

In this article, a novel approach for merging 3D point cloud maps in the context of egocentric multi-robot exploration is presented. Unlike traditional methods, the proposed approach leverages state-of-the-art place recognition and learned descriptors to efficiently detect overlap between maps, eliminating the need for the time-consuming global feature extraction and feature matching process. The estimated overlapping regions are used to calculate a homogeneous rigid transform, which serves as an initial condition for the GICP point cloud registration algorithm to refine the alignment between the maps. The advantages of this approach include faster processing time, improved accuracy, and increased robustness in challenging environments. Furthermore, the effectiveness of the proposed framework is successfully demonstrated through multiple field missions of robot exploration in a variety of different underground environments.