Immersive Virtual Environment systems that utilize Head Mounted Displays and a large tracking area have the advantage of being able to use natural walking as a locomotion interface. In such systems, difficulties arise when the virtual world is larger than the tracking area and users approach area boundaries. Redirected walking (RDW) is a technique that distorts the correspondence between physical and virtual world motion to steer users away from boundaries and obstacles, including other co-immersed users. Recently, a RDW algorithm was proposed based on the use of artificial potential fields (APF), in which walls and obstacles repel the user. APF-RDW effectively supports multiple simultaneous users and, unlike other RDW algorithms, can easily account for tracking area dimensions and room shape when generating steering instructions. This work investigates the performance of a refined APF-RDW algorithm in different sized tracking areas and in irregularly shaped rooms, as compared to a Steer-to-Center (STC) algorithm and an un-steered control condition. Data was generated in simulation using logged paths of prior live users, and is presented for both single-user and multi-user scenarios. Results show the ability of APF-RDW to steer effectively in irregular concave shaped tracking areas such as L-shaped rooms or crosses, along with scalable multi-user support, and better performance than STC algorithms in almost all conditions.

Head-mounted displays (HMDs) and large area position tracking systems can enable users to navigate virtual worlds through natural walking. Redirected walking (RDW) imperceptibly steers immersed users away from physical world obstacles allowing them to explore unbounded virtual worlds while walking in limited physical space. In cases of imminent collisions, resetting techniques can reorient them into open space. This work introduces categorically new RDW and resetting algorithms based on the use of artificial potential fields that "push" users away from obstacles and other users. Data from human subject experiments indicate that these methods reduce potential single-user resets by 66% and increase the average distance between resets by 86% compared to previous techniques. A live multi-user study demonstrates the viability of the algorithm with up to 3 concurrent users, and simulation results indicate that the algorithm scales efficiently up to at least 8 users and is effective with larger groups.

Redirected walking algorithms imperceptibly rotate a virtual scene about users of immersive virtual environment systems in order to guide them away from tracking area boundaries. Ideally, these distortions permit users to explore large unbounded virtual worlds while walking naturally within a physically limited space. Many potential virtual worlds are composed of corridors, passageways, or aisles. Assuming users are not expected to walk through walls or other objects within the virtual world, these constrained worlds limit the directions of travel and as well as the number of opportunities to change direction. The resulting differences in user movement characteristics within the physical world have an impact on redirected walking algorithm performance. This work presents a comparison of generalized RDW algorithm performance within a constrained virtual world. In contrast to previous studies involving unconstrained virtual worlds, experimental results indicate that the steer-to-orbit keeps users in a smaller area than the steer-to-center algorithm. Moreover, in comparison to steer-to-center, steer-to-orbit is shown to reduce potential wall contacts by over 29%.

Head-mounted display (HMD) based immersive virtual environment (VE) systems that incorporate a wearable rendering unit allow users to navigate within virtual worlds through natural walking. Redirected walking (RDW) is a technique that allows users to explore virtual worlds which are larger than the physical tracking area. It involves imperceptibly rotating the VE that the user sees, which causes the user to subconsciously compensate by physically turning. This work extends generalized RDW techniques to allow two immersed users to share a tracking area. The extension forecasts potential collisions so they can be avoided by using RDW techniques. In simulations based on recorded user-data, unsafe situations which could result in a collision occurred at a rate of 31.5/hr. in baseline experiments. The algorithm presented here resulted in all potential collisions being predicted in advance. Once predicted, most future collisions could be avoided using RDW techniques. Some, however, were resolved by stopping one or both users. These instances occurred at a rate of only 2.5/hr.

Redirected walking algorithms imperceptibly rotate a virtual scene and scale movements to guide users of immersive virtual environment systems away from tracking area boundaries. These distortions ideally permit users to explore large and potentially unbounded virtual worlds while walking naturally through a physically limited space. Estimates of the physical space required to perform effective redirected walking have been based largely on the ability of humans to perceive the distortions introduced by redirected walking and have not examined the impact the overall steering strategy used. This work compares four generalized redirected walking algorithms, including Steer-to-Center, Steer-to-Orbit, Steer-to-Multiple-Targets and Steer-to-Multiple+Center. Two experiments are presented based on simulated navigation as well as live-user navigation carried out in a large immersive virtual environment facility. Simulations were conducted with both synthetic paths and previously-logged user data. Primary comparison metrics include mean and maximum distances from the tracking area center for each algorithm, number of wall contacts, and mean rates of redirection. Results indicated that Steer-to-Center out-performed all other algorithms relative to these metrics. Steer-to-Orbit also performed well in some circumstances.

A goal of redirected walking (RDW) is to allow large virtual worlds to be explored within small tracking areas. Generalized steering algorithms, such as steer-to-center, simply move the user toward locations that are considered to be collision free in most cases. The algorithm developed here, FORCE, identifies collision-free paths by using a map of the tracking area's shape and obstacles, in addition to a multistep, probabilistic prediction of the user's virtual path through a known virtual environment. In the present implementation, the path predictions describe a user's possible movements through a virtual store with aisles. Based on both the user's physical and virtual location / orientation, a search-based optimization technique identifies the optimal steering instruction given the possible user paths. Path prediction uses the map of the virtual world; consequently, the search may propose steering instructions that put the user close to walls if the user's future actions eventually lead away from the wall. Results from both simulated and real users are presented. FORCE identifies collision-free paths in 55.0 percent of the starting conditions compared to 46.1 percent for generalized methods. When considering only the conditions that result in different outcomes, redirection based on FORCE produces collision-free path 94.5 percent of the time.

This paper presents a heading drift correction method and experimental results for position tracking of human movement based on the use of foot-mounted inertial/magnetic sensor modules. A position tracking algorithm was previously developed, which incorporated a zero velocity update technique for correcting accelerometer drift. Previous experiments indicated the presence of a persistent heading drift in the estimated position. In this paper, a simple method for correcting this drift is presented. The method requires the user to walk over a closed loop path with the foot-mounted sensor module. Assuming a constant sensor bias for this initial walk, the resulting position error is then used to accomplish an in situ correction for position estimates during future walks. Experimental results validate the effectiveness of the drift correction method and show a significant improvement in position tracking accuracy. Accuracy is determined based on the final position estimates following walks of 100 and 400 meters. Estimated distance traveled averages within 0.2% of actual distance traveled and distance from the actual position averages within 0.28% of actual distance traveled.

We implement and describe a computer simulation system that takes virtual reality technology beyond specialized laboratories and research sites, and makes them available in any space, such as a high-school gymnasium or a public park. Our hardware and software systems enable HMD-based immersive virtual reality simulations to be conducted in any arbitrary location with no external infrastructure and little-to-no setup required. We demonstrate the ability of this system to provide realistically motion-tracked navigation for users and to generate usable behavioral data by having participants navigate through a full-scale virtual grocery store while physically situated in a grassy field. Applications for behavioral research and use cases for other fields are discussed.

Redirected walking has gained popularity in recent years as a way of enhancing the safety of users immersed in a virtual reality simulation and of extending the amount of space that can be simulated in a virtual environment (VE). Limits imposed by the available physical space and functional tracking area are overcome by inducing immersed users to veer imperceptibly in a way that prevents them from leaving the confines of the tracking space. Redirected walking has been shown to be feasible at levels below noticeable thresholds and to function without increasing the incidence of simulator sickness. The present studies demonstrate that redirected walking can function without negatively impacting memory for spatial locations of landmarks in a VE, despite introducing discrepancies between various spatial senses and distorting the spatial mapping of movement onto the environment. Additionally, the present studies implement what, to our knowledge, is the first generalized redirected walking algorithm that is independent of any task or environment structure, and can adaptively steer users in real time as they engage in spontaneous, unconstrained navigation. The studies also demonstrate that such an algorithm can be implemented successfully in a gymnasium-sized space.