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...
Arthur Daniel Costea, Andra Petrovai, Sergiu Nedevschi Proceedings of 2018 IEEE 21th International Conference on Intelligent Transportation Systems (ITSC 2018), Maui, Hawaii, USA, 4-7 Nov. 2018, pp. 3469-3475 A powerful scene understanding can be achieved by combining the tasks of semantic segmentation and instance level recognition. Considering that these tasks are complementary, we propose a multi-objective fusion scheme which leverages the capabilities of each task: pixel level semantic segmentation performs well in background classification and delimiting foreground objects from background, while instance level segmentation excels in recognizing and classifying objects as a whole. We use a fully convolutional residual network...
R. Varga, A.D. Costea, H. Florea, I. Giosan, S. Nedevschi Proceedings of 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC 2017), Yokohama, Japan, 16-19 Oct. 2017, pp. 1-8 This paper describes a super-sensor that enables 360-degree environment perception for automated vehicles in urban traffic scenarios. We use four fisheye cameras, four 360-degree LIDARs and a GPS/IMU sensor mounted on an automated vehicle to build a super-sensor that offers an enhanced low-level representation of the environment by harmonizing all the available sensor measurements. Individual sensors cannot provide a robust 360-degree perception due to their limitations: field of view, range,...
V.C. Miclea, S. Nedevschi Proceedings of 2017 IEEE Intelligent Vehicles Symposium (IV 17), Los Angeles, CA, USA, 11-14 June 2017, pp. 1795-1802 Lately stereo matching has become a key aspect in autonomous driving, providing highly accurate solutions at relatively low cost. Top approaches on state of the art benchmarks rely on learning mechanisms such as convolutional neural networks (ConvNets) to boost matching accuracy. We propose a new real-time stereo reconstruction method that uses a ConvNet for semantically segmenting the driving scene. In a ”divide and conquer” approach this segmentation enables us to split the large heterogeneous traffic scene into smaller...
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...
