maplab 2.0 - A Modular and Multi-Modal Mapping Framework
METADATA ONLY
Loading...
Author / Producer
Date
2023-02
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
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 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.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
8 (2)
Pages / Article No.
520 - 527
Publisher
IEEE
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
SLAM; mapping; multi-robot SLAM
Organisational unit
Notes
Funding
871542 - PILOTs for robotic INspection and maintenance Grounded on advanced intelligent platforms and prototype applications (EC)