Redirected Walking in Static and Dynamic Scenes Using Visibility Polygons

TL;DR

This paper introduces a novel redirected walking method using visibility polygons and motion planning concepts to guide users along collision-free paths in static and dynamic scenes, reducing resets compared to existing methods.

Contribution

It presents a mathematical framework and an efficient algorithm leveraging visibility polygons for improved redirected walking in complex environments.

Findings

Fewer resets than state-of-the-art algorithms in static scenes.

Effective guidance in dynamic scenes with fewer user disruptions.

Utilizes visibility polygons for concise environment representation.

Abstract

We present a new approach for redirected walking in static and dynamic scenes that uses techniques from robot motion planning to compute the redirection gains that steer the user on collision-free paths in the physical space. Our first contribution is a mathematical framework for redirected walking using concepts from motion planning and configuration spaces. This framework highlights various geometric and perceptual constraints that tend to make collision-free redirected walking difficult. We use our framework to propose an efficient solution to the redirection problem that uses the notion of visibility polygons to compute the free spaces in the physical environment and the virtual environment. The visibility polygon provides a concise representation of the entire space that is visible, and therefore walkable, to the user from their position within an environment. Using this representation of walkable space, we apply redirected walking to steer the user to regions of the visibility polygon in the physical environment that closely match the region that the user occupies in the visibility polygon in the virtual environment. We show that our algorithm is able to steer the user along paths that result in significantly fewer resets than existing state-of-the-art algorithms in both static and dynamic scenes. Our project website is available at https://gamma.umd.edu/vis_poly/.