Drift-correction techniques for scale-adaptive VR navigation

Montano Murillo, Roberto A, Cornelio Martinez, Patricia I, Subramanian, Sriram and Martinez Plasenia, Diego (2019) Drift-correction techniques for scale-adaptive VR navigation. UIST 2019 32nd ACM User Interface Software and Technology Symposium, New Orleans, USA, 20 - 23 October, 2019. Published in: Proceedings of the 32nd ACM User Interface Software and Technology Symposium (UIST 2019). 1123-1135. Association for Computing Machinery ISBN 978-1-4503-6816-2

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Abstract

Scale adaptive techniques for VR navigation enable users to navigate spaces larger than the real space available, while allowing precise interaction when required. However, due to these techniques gradually scaling displacements as the user moves (changing user's speed), they introduce a Drift effect. That is, a user returning to the same point in VR will not return to the same point in the real space. This mismatch between the real/virtual spaces can grow over time, and turn the techniques unusable (i.e., users cannot reach their target locations). In this paper, we characterise and analyse the effects of Drift, highlighting its potential detrimental effects. We then propose two techniques to correct Drift effects and use a data driven approach (using navigation data from real users with a specific scale adaptive technique) to tune them, compare their performance and chose an optimum correction technique and configuration. Our user study, applying our technique in a different environment and with two different scale adaptive navigation techniques, shows that our correction technique can significantly reduce Drift effects and extend the life-span of the navigation techniques (i.e., time that they can be used before Drift draws targets unreachable), while not hindering users' experience.

Item Type: Conference Proceedings
Keywords: Virtual Reality; Navigation; Drift correction.
Schools and Departments: School of Engineering and Informatics > Informatics
Research Centres and Groups: Creative Technology
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Depositing User: Lucy Arnold
Date Deposited: 13 Aug 2019 12:18
Last Modified: 01 Nov 2019 16:18
URI: http://sro.sussex.ac.uk/id/eprint/85436

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