Deliverable 5.2

First development and integration cycle of lifelong mapping

This deliverable describes the lifelong mapping framework after the first development & integration cycle. All components, notably the metric and semantic map, the metric online localization, the semantic data aggregation and the map summarization are functional and integrated on the vehicles, fulfill their basic purposes and interact with each other in a limited fashion. All components deliver first evaluation results.


Modular Sensor Fusion for Semantic Segmentation

Hermann Blum, Abel Gawel, Roland Siegwart and Cesar Cadena

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018

Sensor fusion is a fundamental process in robotic systems as it extends the perceptual range and increases robustness in real-world operations. Current multi-sensor deep learning based semantic segmentation approaches do not provide robustness to under-performing classes in one modality, or require a specific architecture with access to the full aligned multi-sensor training data. In this work, we analyze statistical fusion approaches for semantic segmentation that overcome these drawbacks while keeping a competitive performance. The studied approaches are modular by construction, allowing to have different training sets per modality and only a much smaller subset is needed to calibrate the statistical models. We evaluate a range of statistical fusion approaches and report their performance against state-of-the-art baselines on both real-world and simulated data. In our experiments, the approach improves performance in IoU over the best single modality segmentation results by up to 5%. We make all implementations and configurations publicly available.

pdf   code

Title = {Modular Sensor Fusion for Semantic Segmentation},
Author = {Blum, H. and Gawel, A. and Siegwart, R. and Cadena, C.},
Fullauthor = {Blum, Hermann and Gawel, Abel and Siegwart, Roland and Cadena, Cesar},
Booktitle = {2018 {IEEE/RSJ} International Conference on Intelligent Robots and Systems ({IROS})}, 
Month = {October},
Year = {2018},

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},

Visual Localization

A pose-graph map with visual landmarks is created from tracking local BRISK features on four fish-eye cameras mounted on the vehicle. Subsequent loop-closure detection, pose-graph relaxation and Bundle Adjustment generates a geometrically consistent map. Through offline localization of further datasets, the map can incorporate multiple sessions and cover a wide spectrum of appearance conditions. The resulting multi-session map can then be used for visual localization across weather and seasonal change in the long-term.

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}}, 

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?


 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},