Map-merging Algorithms for Visual SLAM: Feasibility Study and Empirical Evaluation
This addresses the map-merging problem for multi-robot systems, but it is incremental as it evaluates existing algorithms.
The study examined the feasibility of merging different visual SLAM maps into a consistent representation, conducting an empirical evaluation in a simulated environment and reporting qualitative and quantitative results.
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.