Andra Petrovai, Arthur D. Costea and Sergiu Nedevschi Proceedings of 2017 IEEE Intelligent Vehicles Symposium (IV 17), Los Angeles, CA, USA, 11-14 June 2017, pp. 448-455 Scene labeling enables very sophisticated and powerful applications for autonomous driving. Training classifiers for this task would not be possible without the existence of large datasets of pixelwise labeled images. Manually annotating a large number of images is an expensive and time consuming process. In this paper, we propose a new semi-automatic annotation tool for scene labeling tailored for autonomous driving. This tool significantly reduces the effort of the annotator and also the time...
Arthur Daniel Costea, Robert Varga and Sergiu Nedevschi Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 17), Honolulu, HI, USA, 21-26 July 2017, pp. 993-1002 In this paper we propose a novel boosting-based sliding window solution for object detection which can keep up with the precision of the state-of-the art deep learning approaches, while being 10 to 100 times faster. The solution takes advantage of multisensorial perception and exploits information from color, motion and depth. We introduce multimodal multiresolution filtering of signal intensity, gradient magnitude and orientation channels, in order to capture structure at multiple scales...
Alessandro Simovic, Ralf Kaestner and Martin Rufli International Conference on Intelligent Robots and Systems (IROS) 2017 – Poster Track We introduce a novel, decentralized architecture facilitating consensual, blockchain-secured computation and verification of data/knowledge. Through the integration of (i) a decentralized content-addressable storage system, (ii) a decentralized communication and time stamping server, and (iii) a decentralized computation module, it enables a scalable, transparent, and semantically interoperable cloud robotics ecosystem, capable of powering the emerging internet of robots. Paper (.pdf) Poster (.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...
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...
