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Dynamic-Aware Autonomous Exploration in Populated Environments
(2021)2021 IEEE International Conference on Robotics and Automation (ICRA)Autonomous exploration allows mobile robots to navigate in initially unknown territories in order to build complete representations of the environments. In many real-life applications, environments often contain dynamic obstacles which can compromise the exploration process by temporarily blocking passages, narrow paths, exits or entrances to other areas yet to be explored. In this work, we formulate a novel exploration strategy capable ...Conference Paper -
Fast Image-Anomaly Mitigation for Autonomous Mobile Robots
(2021)2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Camera anomalies like rain or dust can severely degrade image quality and its related tasks, such as localization and segmentation. In this work we address this important issue by implementing a pre-processing step that can effectively mitigate such artifacts in a real-time fashion, thus supporting the deployment of autonomous systems with limited compute capabilities. We propose a shallow generator with aggregation, trained in an adversarial ...Conference Paper -
Dynamic Object Aware LiDAR SLAM based on Automatic Generation of Training Data
(2021)2021 IEEE International Conference on Robotics and Automation (ICRA)Highly dynamic environments, with moving objects such as cars or humans, can pose a performance challenge for LiDAR SLAM systems that assume largely static scenes. To overcome this challenge and support the deployment of robots in real world scenarios, we propose a complete solution for a dynamic object aware LiDAR SLAM algorithm. This is achieved by leveraging a real-time capable neural network that can detect dynamic objects, thus ...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 -
VIZARD: Reliable Visual Localization for Autonomous Vehicles in Urban Outdoor Environments
(2019)2019 IEEE Intelligent Vehicles Symposium (IV)Conference Paper -
Optimization-Based Terrain Analysis and Path Planning in Unstructured Environments
(2019)2019 International Conference on Robotics and Automation (ICRA)Conference Paper -
Redundant Perception and State Estimation for Reliable Autonomous Racing
(2019)2019 International Conference on Robotics and Automation (ICRA)Conference Paper -
Design of an Autonomous Racecar: Perception, State Estimation and System Integration
(2018)Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA)Conference Paper -
PoseMap: Lifelong, Multi-Environment 3D LiDAR Localization
(2018)2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Reliable long-term localization is key for robotic systems in dynamic environments. In this paper, we propose a novel approach for long-term localization using 3D LiDARs, coined PoseMap. In essence, we extract distinctive features from range measurements and bundle these into local views along with observation poses. The sensor's trajectory is then estimated in a sliding window fashion by matching current and old features and minimizing ...Conference Paper