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

31 May 2021  ·  Andrey Bokovoy, Kirill Muravyev, Konstantin Yakovlev ·

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}. read more

PDF Abstract

Datasets


Introduced in the Paper:

MAOMaps

Used in the Paper:

Matterport3D TUM RGB-D KAIST Urban

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here