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. https://github.com/ethz-asl/maplab
Danila Rukhovich, Daniel Mouritzen, Ralf Kaestner, Martin Rufli, Alexander Velizhev International Conference on Computer Vision (ICCV) 2019 – Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving This paper addresses the problem of scale estimation in monocular SLAM by estimating absolute distances between camera centers of consecutive image frames. These estimates would improve the overall performance of classical(not deep) SLAM systems and allow metric feature locations to be recovered from a single monocular camera. We propose several network architectures that lead to an improvement of scale estimation accuracy over the state of the art. In addition, we exploit...
Mathias Bürki, Cesar Cadena, Igor Gilitschenski, Roland Siegwart and Juan Nieto Journal of Fields Robotics (JFR) 2019 Visual localization in outdoor environments is subject to varying appearance conditions rendering it difficult to match current camera images against a previously recorded map. Although it is possible to extend the respective maps to allow precise localization across a wide range of differing appearance conditions, these maps quickly grow in size and become impractical to handle on a mobile robotic platform. To address this problem, we present a landmark selection algorithm that exploits appearance co‐observability for efficient visual localization in outdoor environments. Based...
Mathias Buerki, Lukas Schaupp, Marcyn Dymczyk, Renaud Dube, Cesar Cadena, Roland Siegwart, and Juan Nieto IEEE Intelligent Vehicles Symposium (IV) 2019 Changes in appearance is one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present VIZARD, a visual localization system for urban outdoor environments. By combining a local localization algorithm with the use of multi-session maps, a high localization recall can be achieved across vastly different appearance conditions. The fusion of the visual localization constraints with wheel-odometry in a state estimation framework further guarantees smooth and accurate pose estimates. In...
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. pdf
Specification of the Map Frontend and Storage Concept This deliverable corresponds to task 5.1, 5.2 and 5.3. It describes the hardware and software requirements and specifications for the mapping and localization frontend and storage concepts in the cloud-based backend. pdf
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...
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...
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...
Mathias Buerki, Igor Gilitschenski, Elena Stumm, Roland Siegwart, and Juan Nieto International Conference on Intelligent Robots and Systems (IROS) 2016 We present an online landmark selection method for efficient and accurate visual localization under changing appearance conditions. The wide range of conditions encountered during long-term visual localization by e.g. fleets of autonomous vehicles offers the potential exploit redundancy and reduce data usage by selecting only those visual cues which are relevant at the given time. Therefore co-observability statistics guide landmark ranking and selection, significantly reducing the amount of information used for localization while maintaining or even improving accuracy. pdf video...
