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Active Model Learning using Informative Trajectories for Improved Closed-Loop Control on Real Robots
(2021)2021 IEEE International Conference on Robotics and Automation (ICRA)Model-based controllers on real robots require accurate knowledge of the system dynamics to perform optimally. For complex dynamics, first-principles modeling is not sufficiently precise, and data-driven approaches can be leveraged to learn a statistical model from real experiments. However, the efficient and effective data collection for such a data-driven system on real robots is still an open challenge. This paper introduces an ...Conference Paper -
Flying corrosion inspection robot for corrosion monitoring of civil structures – First results
(2019)SMAR 2019 - Fifth Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures - ProgramPotential mapping permits an early detection of corrosion and has major advantages over a purely visual condition assessment. The current manner of assessing the corrosion state of reinforced concrete structures with potential mapping is limited due to the lack of accessibility, leading to high involvement of manpower and finally to high inspection costs. A main challenge in the coming decades will be the assessment of our ageing ...Conference Paper -
Generative Object Detection and Tracking in 3D Range Data
(2012)2012 IEEE International Conference on Robotics and AutomationThis paper presents a novel approach to tracking dynamic objects in 3D range data. Its key contribution lies in the generative object detection algorithm which allows the tracker to robustly extract objects of varying sizes and shapes from the observations. In contrast to tracking methods using discriminative detectors, we are thus able to generalize over a wide range of object classes matching our assumptions. Whilst the generative model ...Conference Paper -
Efficient volumetric mapping of multi-scale environments using wavelet-based compression
(2023)Proceedings of Robotics: Science and System XIXVolumetric maps are widely used in robotics due to their desirable properties in applications such as path planning, exploration, and manipulation. Constant advances in mapping technologies are needed to keep up with the improvements in sensor technology, generating increasingly vast amounts of precise measurements. Handling this data in a computationally and memory-efficient manner is paramount to representing the environment at the ...Conference Paper -
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Incremental Object Database: Building 3D Models from Multiple Partial Observations
(2018)2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Collecting 3D object data sets involves a large amount of manual work and is time consuming. Getting complete models of objects either requires a 3D scanner that covers all the surfaces of an object or one needs to rotate it to completely observe it. We present a system that incrementally builds a database of objects as a mobile agent traverses a scene. Our approach requires no prior knowledge of the shapes present in the scene. Object-like ...Conference Paper -
SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding
(2023)Springer Proceedings in Advanced Robotics ~ Robotics ResearchIn order to operate in human environments, a robot's semantic perception has to overcome open-world challenges such as novel objects and domain gaps. Autonomous deployment to such environments therefore requires robots to update their knowledge and learn without supervision. We investigate how a robot can autonomously discover novel semantic classes and improve accuracy on known classes when exploring an unknown environment. To this end, ...Conference Paper -
Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning
(2021)Proceedings of Machine Learning Research ~ Proceedings of the 2020 Conference on Robot LearningVisual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target. In this work we present a novel approach to obtain favorable trajectories for visual-inertial system calibration, using model-based deep reinforcement learning. Our key contribution is to model the calibration process as a Markov decision ...Conference Paper -
DP-Fusion: A generic framework for online multi sensor recognition
(2012)2012 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)Multi sensor fusion has been widely used in recognition problems. Most existing works highly depend on the calibration between different sensors, but less on modeling and reasoning of the co-incidence of multiple hints. In this paper, we propose a generic framework for recognition and clustering problem using a non-parametric Dirichlet hierarchical model, named DP-Fusion. It enables online labeling, clustering and recognition of sequential ...Conference Paper -
A Lane Detection Vision Module for Driver Assistance
(2004)Mechatronics and robotics 2004 : Aachen, Germany, September 13 - 15, 2004Conference Paper