maplab 一个开放，面向研究用C++编写的可视惯性映射框架

maplab 一个开放，面向研究用C++编写的可视惯性映射框架

News

May 2018: maplab was presented at ICRA in Brisbane. (paper)March 2018: Check out our release candidate with improved localization and lots of new features! PR

Description

This repository contains maplab, an open, research-oriented visual-inertial mapping framework, written in C++, for creating, processing and manipulating multi-session maps. On the one hand, maplab can be considered 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, ROVIOLI, that can create visual-inertial maps and also track a global drift-free pose within a localization map.

For documentation, tutorials and datasets, please visit the wiki.

Please also check out our video:

Features

Robust visual-inertial odometry with localization

Large-scale multisession mapping and optimization

Dense reconstruction

A research platform extensively tested on real robots

Installation and getting started

The following articles help you with getting started with maplab and ROVIOLI:

Installation on Ubuntu 14.04 or 16.04Introduction to the maplab frameworkStructure of the frameworkRunning ROVIOLI in VIO modeBasic console usageConsole map management

More detailed information can be found in the wiki pages.

Research Results

The maplab framework has been used as an experimental platform for numerous scientific publications. For a complete list of publications please refer to Research based on maplab.

Citing

Please cite the following paper when using maplab or ROVIOLI for your research:

@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}, doi={10.1109/LRA.2018.2800113}}

Additional Citations

Certain components of maplab are directly using the code of the following publications:

Localization: @inproceedings{lynen2015get, title={Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization.}, author={Lynen, Simon and Sattler, Torsten and Bosse, Michael and Hesch, Joel A and Pollefeys, Marc and Siegwart, Roland}, booktitle={Robotics: Science and Systems}, year={2015}} ROVIOLI which is composed of ROVIO + maplab for map building and localization: @article{bloesch2017iterated, title={Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback}, author={Bloesch, Michael and Burri, Michael and Omari, Sammy and Hutter, Marco and Siegwart, Roland}, journal={The International Journal of Robotics Research}, volume={36}, number={10}, pages={1053--1072}, year={2017}, publisher={SAGE Publications Sage UK: London, England}}

Credits

Thomas SchneiderMarcin DymczykMarius FehrKevin EggerSimon LynenMathias BürkiTitus CieslewskiTimo HinzmannMathias Gehrig

For a complete list of contributors, have a look at CONTRIBUTORS.md

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