9 papers
Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging
Samim Ahmadi, Linh Kästner, Jan Christian Hauffen +2
This paper presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super resolution (SR) imaging. Photothermal imaging is a well-know…
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators
Linh Kästner, Teham Buiyan, Xinlin Zhao +3
Deep Reinforcement Learning has emerged as an efficient dynamic obstacle avoidance method in highly dynamic environments. It has the potential to replace overly conservative or ine…
Spatial Imagination With Semantic Cognition for Mobile Robots
Zhengcheng Shen, Linh Kästner, Jens Lambrecht
The imagination of the surrounding environment based on experience and semantic cognition has great potential to extend the limited observations and provide more information for ma…
Classification of Spot-welded Joints in Laser Thermography Data using Convolutional Neural Networks
Linh Kästner, Samim Ahmadi, Florian Jonietz +4
Spot welding is a crucial process step in various industries. However, classification of spot welding quality is still a tedious process due to the complexity and sensitivity of th…
Integrative Object and Pose to Task Detection for an Augmented-Reality-based Human Assistance System using Neural Networks
Linh Kästner, Leon Eversberg, Marina Mursa +1
As a result of an increasingly automatized and digitized industry, processes are becoming more complex. Augmented Reality has shown considerable potential in assisting workers with…
Deep-Reinforcement-Learning-Based Semantic Navigation of Mobile Robots in Dynamic Environments
Linh Kästner, Cornelius Marx, Jens Lambrecht
Mobile robots have gained increased importance within industrial tasks such as commissioning, delivery or operation in hazardous environments. The ability to autonomously navigate…