USegScene: Unsupervised Learning of Depth, Optical Flow and Ego-Motion with Semantic Guidance and Coupled Networks

In this paper we propose USegScene, a framework for semantically guided unsupervised learning of depth, optical flow and ego-motion estimation for stereo camera images using convolutional neural networks. Our framework leverages semantic information for improved regularization of depth and optical flow maps, multimodal fusion and occlusion filling considering dynamic rigid object motions as independent SE(3) transformations. Furthermore, complementary to pure photo-metric matching, we propose matching of semantic features, pixel-wise classes and object instance borders between the consecutive images. In contrast to previous methods, we propose a network architecture that jointly predicts all outputs using shared encoders and allows passing information across the task-domains, e.g., the prediction of optical flow can benefit from the prediction of the depth. Furthermore, we explicitly learn the depth and optical flow occlusion maps inside the network, which are leveraged in order to improve the predictions in therespective regions. We present results on the popular KITTI dataset and show that our approach outperforms other methods by a large margin

Learning Object Placements For Relational Instructions by Hallucinating Scene Representations

Robots coexisting with humans in their environment and performing services for them need the ability to interact with them. One particular requirement for such robots is that they are able to understand spatial relations and can place objects in accordance with the spatial relations expressed by their user. In this work, we present a convolutional neural network for estimating pixelwise object placement probabilities for a set of spatial relations from a single input image. During training, our network receives the learning signal by classifying hallucinated high-level scene representations as an auxiliary task. Unlike previous approaches, our method does not require ground truth data for the pixelwise relational probabilities or 3D models of the objects, which significantly expands the applicability in practical applications. Our results obtained using real-world data and human-robot experiments demonstrate the effectiveness of our method in reasoning about the best way to place objects to reproduce a spatial relation. Videos of our experiments can be found at https://youtu.be/zaZkHTWFMKMComment: Accepted at the 2020 IEEE International Conference on Robotics and Automation (ICRA). Video at https://www.youtube.com/watch?v=zaZkHTWFMK

Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans

Due to their ubiquity and long-term stability, pole-like objects are well suited to serve as landmarks for vehicle localization in urban environments. In this work, we present a complete mapping and long-term localization system based on pole landmarks extracted from 3-D lidar data. Our approach features a novel pole detector, a mapping module, and an online localization module, each of which are described in detail, and for which we provide an open-source implementation at www.github.com/acschaefer/polex. In extensive experiments, we demonstrate that our method improves on the state of the art with respect to long-term reliability and accuracy: First, we prove reliability by tasking the system with localizing a mobile robot over the course of 15~months in an urban area based on an initial map, confronting it with constantly varying routes, differing weather conditions, seasonal changes, and construction sites. Second, we show that the proposed approach clearly outperforms a recently published method in terms of accuracy.Comment: 9 page