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LCD – Line Clustering and Description for Place Recognition
(2020)2020 International Conference on 3D Vision (3DV)Current research on visual place recognition mostly focuses on aggregating local visual features of an image into a single vector representation. Therefore, high-level information such as the geometric arrangement of the features is typically lost. In this paper, we introduce a novel learning-based approach to place recognition, using RGB-D cameras and line clusters as visual and geometric features. We state the place recognition problem ...Conference Paper -
MOZARD: Multi-Modal Localization for Autonomous Vehicles in Urban Outdoor Environments
(2020)2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Visually poor scenarios are one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present MOZARD, a multi-modal localization system for urban outdoor environments using vision and LiDAR. By fusing key point based visual multi-session information with semantic data, an improved localization recall can be achieved across vastly different appearance conditions. In particular ...Conference Paper -
Accurate Mapping and Planning for Autonomous Racing
(2020)2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)This paper presents the perception, mapping, and planning pipeline implemented on an autonomous race car. It was developed by the 2019 AMZ driverless team for the Formula Student Germany (FSG) 2019 driverless competition, where it won 1st place overall. The presented solution combines early fusion of camera and LiDAR data, a layered mapping approach, and a planning approach that uses Bayesian filtering to achieve high-speed driving on ...Conference Paper -
MultiPoint: Cross-spectral registration of thermal and optical aerial imagery
(2020)Proceedings of Machine Learning Research ~ Proceedings of the 2020 Conference on Robot LearningWhile optical cameras are ubiquitous in robotics, some robots can sense the world in several sections of the electromagnetic spectrum simultaneously, which can extend their capabilities in fundamental ways. For instance, many fixed-wing UAVs carry both optical and thermal imaging cameras, potentially allowing them to detect temperature difference-induced atmospheric updrafts, map their locations, and adjust their flight path accordingly ...Conference Paper -
IAN: Multi-behavior navigation planning for robots in real, crowded environments
(2020)2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)State-of-the-art approaches for robot navigation among humans are typically restricted to planar movement actions. This work addresses the question of whether it can be beneficial to use interaction actions, such as saying, touching, and gesturing, for the sake of allowing robots to navigate in unstructured, crowded environments. To do so, we first identify challenging scenarios to traditional motion planning methods. Based on the hypothesis ...Conference Paper -
Leveraging Stereo-Camera Data for Real-Time Dynamic Obstacle Detection and Tracking
(2020)2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Dynamic obstacle avoidance is one crucial component for compliant navigation in crowded environments. In this paper we present a system for accurate and reliable detection and tracking of dynamic objects using noisy point cloud data generated by stereo cameras. Our solution is real-time capable and specifically designed for the deployment on computationally-constrained unmanned ground vehicles. The proposed approach identifies individual ...Conference Paper -
Robot Navigation in Crowded Environments Using Deep Reinforcement Learning
(2020)2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Mobile robots operating in public environments require the ability to navigate among humans and other obstacles in a socially compliant and safe manner. This work presents a combined imitation learning and deep reinforcement learning approach for motion planning in such crowded and cluttered environments. By separately processing information related to static and dynamic objects, we enable our network to learn motion patterns that are ...Conference Paper -
A Data-driven Planning Framework for Robotic Texture Painting on 3D Surfaces
(2020)2020 IEEE International Conference on Robotics and Automation (ICRA)Painting textures on 3D surfaces requires an understanding of the surface geometry, paint flow and paint mixing. This work formulates automated painting as a planning problem and proposes a solution based on a self-supervised learning framework that enables a robot to paint monochromatic non-uniform textures on 3D surfaces. We developed a method that iteratively decides the actions to take based on constant feedback of the painting process. ...Conference Paper -
A Connectivity-Prediction Algorithm and its Application in Active Cooperative Localization for Multi-Robot Systems
(2020)2020 IEEE International Conference on Robotics and Automation (ICRA)This paper presents a method for predicting the probability of future connectivity between mobile robots with range-limited communication. In particular, we focus on its application to active motion planning for cooperative localization (CL). The probability of connection is modeled by the distribution of quadratic forms in random normal variables and is computed by the infinite power series expansion theorem. A finite-term approximation ...Conference Paper -
Learning Common and Transferable Feature Representations for Multi-Modal Data
(2020)2020 IEEE Intelligent Vehicles Symposium (IV)LiDAR sensors are crucial in automotive perception for accurate object detection. However, LiDAR data is hard to interpret for humans and consequently time-consuming to label. Whereas camera data is easy interpretable and thus, comparably simpler to label. Within this work we present a transductive transfer learning approach to transfer the knowledge for the object detection task from images to point cloud data. We propose a multi-modal ...Conference Paper