Map Management for Efficient Long-Term Visual Localization in Outdoor Environments

Mathias Buerki, Marcyn Dymczyk, Igor Gilitschenski, Cesar Cadena, Roland Siegwart, and Juan Nieto

IEEE Intelligent Vehicles Symposium (IV) 2018

We present a complete map management process for a visual localization system designed for multi-vehicle long-term operations in resource constrained outdoor environments. Outdoor visual localization generates large amounts of data that need to be incorporated into a lifelong visual map in order to allow localization at all times and under all appearance conditions. Processing these large quantities of data is nontrivial, as it is subject to limited computational and storage capabilities both on the vehicle and on the mapping back-end. We address this problem with a two-fold map update paradigm capable of, either, adding new visual cues to the map, or updating co-observation statistics. The former, in combination with offline map summarization techniques, allows enhancing the appearance coverage of the lifelong map while keeping the map size limited. On the other hand, the latter is able to significantly boost the appearance-based landmark selection for efficient online localization without incurring any additional computational or storage burden. Our evaluation in challenging outdoor conditions shows that our proposed map management process allows building and maintaining maps for precise visual localization over long time spans in a tractable and scalable fashion

pdf   video

Title = {Map Management for Efficient Long-Term Visual Localization in Outdoor Environments},
Author = {M. Buerki and M. Dymczyk and I. Gilitschenski and C. Cadena and R. Siegwart and J. Nieto},
Fullauthor = {Mathias Buerki and Marcyn Dymczyk and Igor Gilitschenski and Cesar Cadena and Roland Siegwart and Juan Nieto},
Booktitle = {{IEEE} Intelligent Vehicles Symposium ({IV})},
Month = {June},
Year = {2018},

Design of an autonomous racecar: Perception, state estimation and system integration

Miguel Valls, Hubertus Hendrikx, Victor Reijgwart, Fabio Meier, Inkyu Sa, Renaud Dube, Abel Gawel, Mathias Bürki and Roland Siegwart

IEEE International Conference on Robotics and Automation (ICRA) 2018

This paper introduces fluela driverless: the first autonomous racecar to win a Formula Student Driverless competition. In this competition, among other challenges, an autonomous racecar is tasked to complete 10 laps of a previously unknown racetrack as fast as possible and using only onboard sensing and computing. The key components of fluela’s design are its modular redundant sub–systems that allow
robust performance despite challenging perceptual conditions or partial system failures. The paper presents the integration of key components of our autonomous racecar, i.e., system design, EKF–based state estimation, LiDAR–based perception, and particle filter-based SLAM. We perform an extensive
experimental evaluation on real–world data, demonstrating the system’s effectiveness by outperforming the next–best ranking team by almost half the time required to finish a lap. The autonomous racecar reaches lateral and longitudinal accelerations comparable to those achieved by experienced human drivers.

pdf    video

  title={Design of an autonomous racecar: Perception, state estimation and system integration},
  author={Valls, Miguel I and Hendrikx, Hubertus FC and Reijgwart, Victor JF and Meier, Fabio V and Sa, Inkyu and Dub{\'e}, Renaud and Gawel, Abel and B{\"u}rki, Mathias and Siegwart, Roland},
  booktitle={2018 IEEE International Conference on Robotics and Automation (ICRA)},

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.


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}},