ARC: Alignment-based Redirection Controller for Redirected Walking in Complex Environments

TL;DR

This paper introduces ARC, a new redirected walking controller that uses alignment to navigate users through complex virtual environments with fewer collisions, outperforming existing methods in simulations and real-world tests.

Contribution

The paper presents ARC, a novel alignment-based redirection controller that effectively manages navigation in complex environments without user input or parameter tuning.

Findings

ARC significantly reduces collisions compared to state-of-the-art controllers.

ARC is robust in environments with many obstacles.

The method operates effectively on consumer VR hardware.

Abstract

We present a novel redirected walking controller based on alignment that allows the user to explore large and complex virtual environments, while minimizing the number of collisions with obstacles in the physical environment. Our alignment-based redirection controller, ARC, steers the user such that their proximity to obstacles in the physical environment matches the proximity to obstacles in the virtual environment as closely as possible. To quantify a controller's performance in complex environments, we introduce a new metric, Complexity Ratio (CR), to measure the relative environment complexity and characterize the difference in navigational complexity between the physical and virtual environments. Through extensive simulation-based experiments, we show that ARC significantly outperforms current state-of-the-art controllers in its ability to steer the user on a collision-free path. We also show through quantitative and qualitative measures of performance that our controller is robust in complex environments with many obstacles. Our method is applicable to arbitrary environments and operates without any user input or parameter tweaking, aside from the layout of the environments. We have implemented our algorithm on the Oculus Quest head-mounted display and evaluated its performance in environments with varying complexity. Our project website is available at https://gamma.umd.edu/arc/.