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.
@article{arxiv.2212.00654,
title = {maplab 2.0 -- A Modular and Multi-Modal Mapping Framework},
author = {Andrei Cramariuc and Lukas Bernreiter and Florian Tschopp and Marius Fehr and Victor Reijgwart and Juan Nieto and Roland Siegwart and Cesar Cadena},
journal= {arXiv preprint arXiv:2212.00654},
year = {2023}
}