Map-merging Algorithms for Visual SLAM: Feasibility Study and Empirical Evaluation

TL;DR

This paper evaluates the feasibility of merging maps generated by visual SLAM algorithms in multi-robot scenarios through extensive empirical testing in simulated environments.

Contribution

It provides a comprehensive empirical comparison of existing 2D and 3D map-merging algorithms for visual SLAM in realistic simulations.

Findings

Some algorithms successfully merge maps in certain scenarios

Performance varies significantly across different algorithms

Insights into the strengths and limitations of current map-merging methods

Abstract

Simultaneous localization and mapping, especially the one relying solely on video data (vSLAM), is a challenging problem that has been extensively studied in robotics and computer vision. State-of-the-art vSLAM algorithms are capable of constructing accurate-enough maps that enable a mobile robot to autonomously navigate an unknown environment. In this work, we are interested in an important problem related to vSLAM, i.e. map merging, that might appear in various practically important scenarios, e.g. in a multi-robot coverage scenario. This problem asks whether different vSLAM maps can be merged into a consistent single representation. We examine the existing 2D and 3D map-merging algorithms and conduct an extensive empirical evaluation in realistic simulated environment (Habitat). Both qualitative and quantitative comparison is carried out and the obtained results are reported and analyzed.