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...
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...
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...
Renaud Dube, Andrei Cramariuc1, Daniel Dugas, Hannes Sommer, Marcin Dymczyk, Juan Nieto, Roland Siegwart, and Cesar Cadena International Journal of Robotics Research (IJRR) 2019 Precisely estimating a robot’s pose in a prior, global map is a fundamental capability for mobile robotics, e.g. autonomous driving or exploration in disaster zones. This task, however, remains challenging in unstructured, dynamic environments, where local features are not discriminative enough and global scene descriptors only provide coarse information. We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds. Working at the level of...
Lukas Bernreiter, Abel Gawel, Hannes Sommer, Juan Nieto, Roland Siegwart and Cesar Cadena IEEE Robotics and Automation Letters, 2019 We present a semantic mapping approach with multiple hypothesis tracking for data association. As semantic information has the potential to overcome ambiguity in measurements and place recognition, it forms an eminent modality for autonomous systems. This is particularly evident in urban scenarios with several similar-looking surroundings. Nevertheless, it requires the handling of a non-Gaussian and discrete random variable coming from object detectors. Previous methods facilitate semantic information for global localization and data association to reduce the instance ambiguity between the landmarks....
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...
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...
