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arXiv · 2212.00654

maplab 2.0 -- A Modular and Multi-Modal Mapping Framework

Andrei Cramariuc·Lukas Bernreiter·Florian Tschopp·Marius Fehr·Victor Reijgwart·Juan Nieto·Roland Siegwart·Cesar Cadena
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Abstract

Integration of multiple sensor modalities and deep learning into Simultaneous Localization And Mapping (SLAM) systems are areas of significant interest in current research. Multi-modality is a stepping stone towards achieving robustness in challenging environments and interoperability of heterogeneous multi-robot systems with varying sensor setups. With maplab 2.0, we provide a versatile open-source platform that facilitates developing, testing, and integrating new modules and features into a fully-fledged SLAM system. Through extensive experiments, we show that maplab 2.0's accuracy is comparable to the state-of-the-art on the HILTI 2021 benchmark. Additionally, we showcase the flexibility of our system with three use cases: i) large-scale (approx. 10 km) multi-robot multi-session (23 missions) mapping, ii) integration of non-visual landmarks, and iii) incorporating a semantic object-based loop closure module into the mapping framework. The code is available open-source at https://github.com/ethz-asl/maplab.

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Andrei Cramariuc, Lukas Bernreiter, Florian Tschopp, Marius Fehr, Victor Reijgwart, Juan Nieto, Roland Siegwart, Cesar Cadena. 2022-12-01. maplab 2.0 -- A Modular and Multi-Modal Mapping Framework. https://doi.org/10.1109/lra.2022.3227865

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