Multiple Hypothesis Semantic Mapping for Robust Data Association

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....

Empty Cities: Image Inpainting for a Dynamic-Object-Invariant Space

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...

Deliverable 9.6

Press video This deliverable provides the UP-Drive press video. The video itself is placed at the landing page of the project webpage at https://www.up-drive.ethz.ch – it is the bottom video. pdf