maplab 2.0 - A Modular and Multi-Modal Mapping Framework

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 fullyfledged 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 (similar to 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.

Automated summary

The integration of multiple sensor modalities and deep learning into SLAM systems is an important area of research. It enables robustness in challenging environments and interoperability of heterogeneous multi-robot systems. Maplab 2.0 provides an open-source platform for developing and integrating new modules and features into a fully-fledged SLAM system. Extensive experiments show that maplab 2.0 achieves comparable accuracy to the state-of-the-art benchmark and demonstrates flexibility in various use cases.