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....
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...
Final specification and design of on-board sensing This deliverable states the updates on two major aspects since Deliverable 4.1: The first aspect consists in the detailed specification of the perception goals and of the sensor model of the environment. The second aspect consists in the selection and the definition of the robust and redundant perception solution for each individual perception task based on the available or new sensors. pdf
