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

TL;DR

Maplab 2.0 is an open-source, modular SLAM framework that integrates multiple sensor modalities, demonstrating high accuracy and flexibility through various complex multi-robot and multi-session mapping use cases.

Contribution

It introduces a versatile, open-source SLAM platform supporting multi-modality, multi-robot, and semantic integration, with extensive experimental validation.

Findings

Accuracy comparable to state-of-the-art on HILTI 2021 benchmark.

Successful large-scale multi-robot multi-session mapping.

Effective integration of non-visual landmarks and semantic modules.

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.