maplab 2.0 -- A Modular and Multi-Modal Mapping Framework (2212.00654v2)

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.

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

The work presented in the paper "maplab 2.0 -- A Modular and Multi-Modal Mapping Framework" introduces a sophisticated platform for simultaneous localization and mapping (SLAM). This platform is tailored for the research community, focusing on multi-modal and multi-robot mapping, supporting both online and offline processing.

Technical Overview

Maplab 2.0 represents an advancement over its predecessor by incorporating multiple sensor modalities such as LiDAR, GPS, and semantic objects, in addition to the traditional visual-inertial sensors. This versatility facilitates robust mapping operations across different environments and improves interoperability for heterogeneous multi-robot systems. The core framework leverages a factor graph structure, utilizing vertices to represent robot states and landmarks, with edges enforcing various constraints derived from sensor observations.

Key Features

1. Multi-Modality and Flexibility: Maplab 2.0 can handle diverse sensors and offers integration for custom features, allowing researchers to conduct experiments without being constrained by specific sensor setups. The framework supports adding relative and absolute constraints seamlessly, promoting ease of experimentation with different mapping techniques.
2. Multi-Robot Mapping: The introduction of a centralized mapping server node enables collaborative mapping from multiple robots. This server collects submaps from individual robots, performs preprocessing including local optimization and loop closure, and builds a globally consistent map. The submap architecture allows effective data management and supports simultaneous multi-robot operation, greatly enhancing computational efficiency.
3. Enhanced Mapping Capabilities: With new modules supporting online collaborative SLAM and detailed multi-modal mapping, maplab 2.0 showcases impressive performance. It can incorporate both visual and non-visual landmarks and supports advanced features such as semantic object-based loop closure, further contributing to the robustness and accuracy of SLAM operations.

Experiments and Results

The framework's capabilities were rigorously tested using the HILTI SLAM Challenge 2021 dataset and in large-scale real-world environments. Results demonstrated that maplab 2.0 not only matches but often exceeds the accuracy of state-of-the-art SLAM systems. Notably, it showed superior performance using diverse feature sets like BRISK, SuperPoint, and SIFT, and could flexibly incorporate the best-performing odometry sources for improved mappings.

Furthermore, the framework proved its practicality in large-scale, multi-robot, multi-session mapping scenarios, navigating complex urban-like environments and handling multiple indoor-outdoor transitions effectively. The ability to conduct LiDAR-based visual tracking and integrate semantic information highlights its adaptability and potential for applications involving complex environmental modeling.

Implications and Future Directions

Maplab 2.0 is positioned as a versatile tool for advancing SLAM research. Its modular nature and open-source availability allow for experimentation with cutting-edge techniques, facilitating developments in robotic mapping, perception, and navigation. Researchers can explore new sensor fusion strategies and improve the robustness and scalability of SLAM solutions.

Looking forward, as SLAM technologies evolve, maplab 2.0 could be extended to adopt emerging sensors and algorithms, ensuring its relevance in future developments. Moreover, its potential integration with deep learning-based perception systems could lead to further innovations in autonomous robotics.

In conclusion, maplab 2.0 significantly contributes to the field of robotics and SLAM research, providing a comprehensive, adaptable, and high-performance platform suitable for a wide range of applications and future explorations in robotic mapping and perception systems.