Semantic Interaction in Augmented Reality Environments for Microsoft HoloLens
This work addresses human-machine interaction challenges in robotics by providing a domain-specific AR system for indoor environments, though it is incremental as it builds on existing segmentation methods.
The paper tackles the problem of enabling semantic interaction in augmented reality for robotics by using Microsoft HoloLens to capture and annotate indoor environments with object classes, allowing users to trigger actions via gestures, with results analyzed for accuracy and performance on an indoor dataset.
Augmented Reality is a promising technique for human-machine interaction. Especially in robotics, which always considers systems in their environment, it is highly beneficial to display visualizations and receive user input directly in exactly that environment. We explore this idea using the Microsoft HoloLens, with which we capture indoor environments and display interaction cues with known object classes. The 3D mesh recorded by the HoloLens is annotated on-line, as the user moves, with semantic classes using a projective approach, which allows us to use a state-of-the-art 2D semantic segmentation method. The results are fused onto the mesh; prominent object segments are identified and displayed in 3D to the user. Finally, the user can trigger actions by gesturing at the object. We both present qualitative results and analyze the accuracy and performance of our method in detail on an indoor dataset.