English

Long-term Large-scale Mapping and Localization Using maplab

Robotics 2018-05-29 v1 Computer Vision and Pattern Recognition

Abstract

This paper discusses a large-scale and long-term mapping and localization scenario using the maplab open-source framework. We present a brief overview of the specific algorithms in the system that enable building a consistent map from multiple sessions. We then demonstrate that such a map can be reused even a few months later for efficient 6-DoF localization and also new trajectories can be registered within the existing 3D model. The datasets presented in this paper are made publicly available.

Keywords

Cite

@article{arxiv.1805.10994,
  title  = {Long-term Large-scale Mapping and Localization Using maplab},
  author = {Marcin Dymczyk and Marius Fehr and Thomas Schneider and Roland Siegwart},
  journal= {arXiv preprint arXiv:1805.10994},
  year   = {2018}
}

Comments

Workshop on Long-term autonomy and deployment of intelligent robots in the real-world, ICRA 2018, Brisbane, Australia

R2 v1 2026-06-23T02:10:41.135Z