Lukas Schaupp, Mathias Buerki, Renaud Dube, Roland Siegwart, and Cesar Cadena IEEE/RJS Int. Conference on Intelligent RObots and Systems (IROS) 2019 We introduce a novel method for oriented place recognition with 3D LiDAR scans. A Convolutional Neural Network is trained to extract compact descriptors from single 3D LiDAR scans. These can be used both to retrieve near-by place candidates from a map, and to estimate the yaw discrepancy needed for bootstrapping local registration methods. We employ a triplet loss function for training and use a hard negative mining strategy to further increase the performance of our descriptor extractor. In an...
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...
Julia Nitsch, Juan Nieto, Roland Siegwart, Max Schmidt, and Cesar Cadena IEEE International Conference on Robotics and Automation (ICRA) 2019 For autonomous driving applications it is critical to know which type of road users and road side infrastructure are present to plan driving manoeuvres accordingly. Therefore autonomous cars are equipped with different sensor modalities to robustly perceive its environment. However, for classification modules based on machine learning techniques it is challenging to overcome unseen sensor noise. This work presents an object classification module operating on unsupervised learned multi-modal features with the ability to overcome gradual or total sensor failure. A...
Berta Bescos, Jose Neira, Roland Siegwart, and Cesar Cadena IEEE International Conference on Robotics and Automation (ICRA) 2019 In this paper we present an end-to-end deep learning framework to turn images that show dynamic content, such as vehicles or pedestrians, into realistic static frames. This objective encounters two main challenges: detecting all the dynamic objects, and inpainting the static occluded background with plausible imagery. The former challenge is addressed by the use of a convolutional network that learns a multiclass semantic segmentation of the image. The second problem is approached with a conditional generative adversarial model that, taking as input...
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...
