  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.
