日本フィジカルAI新聞

世界のフィジカルAIを、日本語で。

週刊ニュースレター購読
arXiv:1805.10994

Long-term Large-scale Mapping and Localization Using maplab

Long-term Large-scale Mapping and Localization Using maplab

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著者: Marcin Dymczyk, Marius Fehr, Thomas Schneider, Roland Siegwart

分類: cs.RO, cs.CV

原文アブストラクト

This paper discusses a large-scale and long-term mapping and localization scenario using the maplab open-source framework. We present a brief overview of the specific algorithms in the system that enable building a consistent map from multiple sessions. We then demonstrate that such a map can be reused even a few months later for efficient 6-DoF localization and also new trajectories can be registered within the existing 3D model. The datasets presented in this paper are made publicly available.