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

Thomas Schneider, Marcin Dymczyk, Marius Fehr, Kevin Egger, Simon Lynen, Igor Gilitschenski and Roland Siegwart

IEEE Robotics and Automation Letters, 2018

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 drift-free 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 by combining state-of-the-art algorithms, scalable multi-session mapping tools, and a flexible user interface, we can create an efficient research platform. 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 multi-session 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.

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@article{schneider2018maplab, 
title={maplab: An Open Framework for Research in Visual-inertial Mapping and Localization}, 
author={T. Schneider and M. T. Dymczyk and M. Fehr and K. Egger and S. Lynen and I. Gilitschenski and R. Siegwart}, 
journal={{IEEE Robotics and Automation Letters}}, 
year={2018} 
}

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Cesar Cadena, Luca Carlone, Henry Carrillo, Yasir Latif, Davide Scaramuzza, Jose Neira, Ian Reid and John J. Leonard

IEEE Transactions on Robotics 32 (6) pp 1309-1332, 2016

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors’ take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?

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@article{Cadena16tro-SLAMfuture,
 title = {Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age},
 author = {C. Cadena and L. Carlone and H. Carrillo and Y. Latif and D. Scaramuzza and J. Neira and I. Reid and J.J. Leonard},
 journal = {{IEEE Transactions on Robotics}},
 year = {2016},
 number = {6},
 pages  = {1309--1332},
 volume = {32},
}