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...
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...
Arthur D. Costea and Sergiu Nedevschi Proceedings of 2017 IEEE Intelligent Vehicles Symposium (IV), Redondo Beach, CA, USA, June 11-14, 2017, pp. 74-81 In this paper we introduce a novel multimodal boosting based solution for semantic segmentation of traffic scenarios. Local structure and context are captured from both monocular color and depth modalities in the form of image channels. We define multiple channel types at three different levels: low, intermediate and high order channels. The low order channels are computed using a multimodal multiresolution filtering scheme and capture structure and color information from lower receptive fields. For the intermediate order...
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...
