M.P. Muresan, S. Nedevschi Proceedings of 2019 15th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 5-7, 2019, pp. 11-18. The unknown correspondences of measurements and targets, referred to as data association, is one of the main challenges of multi-target tracking. Each new measurement received could be the continuation of some previously detected target, the first detection of a new target or a false alarm. Tracking 3D cuboids, is particularly difficult due to the high amount of data, which can include erroneous or noisy information coming from sensors, that can lead to false measurements, detections...
A. Petrovai, S. Nedevschi Proceeding of 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zeeland, 26-30 October,2019, pp. 2394-2401. In this paper, we tackle the newly introduced panoptic segmentation task. Panoptic segmentation unifies semantic and instance segmentation and leverages the capabilities of these complementary tasks by providing pixel and instance level classification. Current state-of-the-art approaches employ either separate networks for each task or a single network for both task and post processing heuristics fuse the outputs into the final panoptic segmentation. Instead, our approach solves all three tasks including panoptic segmentation with an end-to-end learnable fully convolutional neural network....
M.P. Muresan, I. Giosan, S. Nedevschi Sensors 2020, 20, 1110; doi:10.3390/s20041110, pp. 1-33. The stabilization and validation process of the measured position of objects is an important step for high‐level perception functions and for the correct processing of sensory data. The goal of this process is to detect and handle inconsistencies between different sensor measurements, which result from the perception system. The aggregation of the detections from different sensors consists in the combination of the sensorial data in one common reference frame for each identified object, leading to the creation of a super‐sensor. The result of the data aggregation may...
S.E.C. Deac, I. Giosan, S. Nedevschi Proceeding of 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zeeland, 26-30 October,2019, pp. 3433-3440. In this paper we propose a robust curb detection method which is based on the fusion between semantically labeled camera images and a 3D point cloud coming from LiDAR sensors. The labels from the semantically enhanced cloud are used to reduce the curbs’ searching area. Several spatial cues are next computed on each candidate curb region. Based on these features, a candidate curb region is either rejected or refined for obtaining a precise positioning of the curb points...
V.C. Miclea, S. Nedevschi IEEE Transactions on Intelligent Transportation Systems (Early Access), pp. 1-11, 2019, DOI: 10.1109/TITS.2019.2913883. In this paper, we propose a novel semantic segmentation-based stereo reconstruction method that can keep up with the accuracy of the state-of-the art approaches while running in real time. The solution follows the classic stereo pipeline, each step in the stereo workflow being enhanced by additional information from semantic segmentation. Therefore, we introduce several improvements to computation, aggregation, and optimization by adapting existing techniques to integrate additional surface information given by each semantic class. For the cost computation and optimization steps, we propose...
A. Petrovai, S. Nedevschi Proceeding of 2019 IEEE Intelligent Vehicles Symposium (IV 2019), Paris, France, 9 – 12 June, 2019, pp. 2575-2581. Environment perception for automated vehicles is achieved by fusing the outputs of different sensors such as cameras, LIDARs and RADARs. Images provide a semantic understanding of the environment at object level using instance segmentation, but also at background level using semantic segmentation. We propose a fully convolutional residual network based on Mask R-CNN to achieve both semantic and instance level recognition. We aim at developing an efficient network that could run in real-time for automated driving applications without...
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...
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...
