MAOMaps: A Photo-Realistic Benchmark For vSLAM and Map Merging Quality Assessment

• URL: http://arxiv.org/abs/2105.14994v1
• Date: Mon, 31 May 2021 14:30:36 GMT
• Title: MAOMaps: A Photo-Realistic Benchmark For vSLAM and Map Merging Quality Assessment
• Authors: Andrey Bokovoy, Kirill Muravyev and Konstantin Yakovlev (Federal Research Center for Computer Science and Control of Russian Academy of Sciences)
• Abstract summary: We introduce a novel benchmark that is aimed at quantitatively evaluating the quality of vision-based simultaneous localization and mapping (vSLAM) and map merging algorithms. The dataset is photo-realistic and provides both the localization and the map ground truth data. To compare the vSLAM-built maps and the ground-truth ones we introduce a novel way to find correspondences between them that takes the SLAM context into account.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Running numerous experiments in simulation is a necessary step before deploying a control system on a real robot. In this paper we introduce a novel benchmark that is aimed at quantitatively evaluating the quality of vision-based simultaneous localization and mapping (vSLAM) and map merging algorithms. The benchmark consists of both a dataset and a set of tools for automatic evaluation. The dataset is photo-realistic and provides both the localization and the map ground truth data. This makes it possible to evaluate not only the localization part of the SLAM pipeline but the mapping part as well. To compare the vSLAM-built maps and the ground-truth ones we introduce a novel way to find correspondences between them that takes the SLAM context into account (as opposed to other approaches like nearest neighbors). The benchmark is ROS-compatable and is open-sourced to the community. The data and the code are available at: \texttt{github.com/CnnDepth/MAOMaps}.

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