Photothermal imaging is a well-known technique in active thermography for nondestructive inspection of defects in materials such as metals or composites.
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 mapping, collision avoidance, and path planning.
Recently, mobile robots have become important tools in various industries, especially in logistics.
Deep Reinforcement Learning has emerged as an efficient dynamic obstacle avoidance method in highly dynamic environments.
More precisely, we show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters.
Spot welding is a crucial process step in various industries.
As a result of an increasingly automatized and digitized industry, processes are becoming more complex.
Visual based navigation and high level semantics bear the potential to enhance the safety of path planing by creating links the agent can reason about for a more flexible navigation.
In this work we propose a method to deploy state of the art neural networks for real time 3D object localization on augmented reality devices.
The results are adaptable to work with all depth cameras and are promising for further research.