Abstract
Augmented reality (AR) lends itself to presenting visual instructions on how to assemble or disassemble an object. Splitting the assembly procedure into shorter steps and presenting the corresponding instructions in AR supports their comprehension. However, one can still misinterpret instructions and make errors while manipulating the object. While previous work supports detecting the occurrence of errors, we investigate handling such errors. This requires knowledge of the error at runtime of the application. Starting from a categorization of the errors, we investigate how to automatically derive common error states to generate training data. We introduce an extension to a state-of-the-art deep-learning-based object detector for supporting the detection of assembly states at real-time update rates, based on contrastive learning. We evaluated the proposed detector, showing that it outperforms the state-of-the-art, and we demonstrate our work with an AR application that alerts the user if errors occur and provides visual help to correct the error.
How It Works
Citation
@proceedings{stanescu2024errormanagement,
author={Stanescu, Ana and Mohr, Peter and Thaler, Franz and Kozinski, Mateusz and Skreinig, Lucchas Ribeiro and Schmalstieg, Dieter and Kalkofen, Denis},
booktitle={IEEE Int. Symp. on Mixed and Augmented Reality (ISMAR)},
title={Error Management for Augmented Reality Assembly Instructions},
year={2024}}
}