maplab: An Open Framework for Research in Visual-inertial Mapping and Localization

TL;DR

maplab is an open, comprehensive framework that enables multi-session visual-inertial mapping and localization, integrating state-of-the-art algorithms with tools for research and practical deployment.

Contribution

It introduces a complete, flexible system combining mapping, localization, and research tools in a unified open-source platform for robotics applications.

Findings

Effective multi-session map merging demonstrated

Accurate drift-free localization within maps

Open-source code available for community use

Abstract

Robust and accurate visual-inertial estimation is crucial to many of today's challenges in robotics. Being able to localize against a prior map and obtain accurate and driftfree pose estimates can push the applicability of such systems even further. Most of the currently available solutions, however, either focus on a single session use-case, lack localization capabilities or an end-to-end pipeline. We believe that only a complete system, combining state-of-the-art algorithms, scalable multi-session mapping tools, and a flexible user interface, can become an efficient research platform. We therefore present maplab, an open, research-oriented visual-inertial mapping framework for processing and manipulating multi-session maps, written in C++. On the one hand, maplab can be seen as a ready-to-use visual-inertial mapping and localization system. On the other hand, maplab provides the research community with a collection of multisession mapping tools that include map merging, visual-inertial batch optimization, and loop closure. Furthermore, it includes an online frontend that can create visual-inertial maps and also track a global drift-free pose within a localization map. In this paper, we present the system architecture, five use-cases, and evaluations of the system on public datasets. The source code of maplab is freely available for the benefit of the robotics research community.