MAOMaps：vSLAMと地図統合の品質評価のための写真リアルなベンチマーク
MAOMaps: A Photo-Realistic Benchmark For vSLAM and Map Merging Quality Assessment
写真リアルなデータセットと自動評価ツールを提供し、vSLAMと地図統合アルゴリズムの品質を定量的に評価できるベンチマークを提案した。
著者: Andrey Bokovoy, Kirill Muravyev, Konstantin Yakovlev
分類: cs.CV, cs.RO
原文アブストラクト
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}.
