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.

Accurate estimation of orientation based on data from small low-cost strapdown inertial and magnetic sensors is often inaccurate during highly dynamic motion or when trying to track movements that include two or more periods characterized by significantly different frequencies. This paper presents a complementary filtering algorithm for estimating orientation based on inertial/magnetic sensor measurements. The algorithm takes advantage of the complementary nature of the information offered by high-frequency angular rate sensor data and low frequency accelerometers and magnetometers. The filtering algorithm utilizes a single gain that can be adaptively adjusted to achieve satisfactory performance while tracking two or more different types of motion. An additional feature of our approach involves the simple estimation of the gyro bias during periods exhibiting low dynamics and its subsequent use to correct the instantaneous gyro measurements. Simulation and experimental results are presented that demonstrate the performance of the algorithm during slow or nearly static movements, as well as, those which are highly dynamic. Experimental results indicate that the algorithm is able to track pitch and roll during dynamic motion with an RMS error of less than two degrees. This is believed to be superior to current proprietary commercial algorithms.

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.

Numerous applications require a self-contained personal navigation system that works in indoor and outdoor environments, does not require any infrastructure support, and is not susceptible to jamming. Posture tracking with an array of inertial/magnetic sensors attached to individual human limb segments has been successfully demonstrated. The "sourceless" nature of this technique makes possible full body posture tracking in an area of unlimited size with no supporting infrastructure. Such sensor modules contain three orthogonally mounted angular rate sensors, three orthogonal linear accelerometers and three orthogonal magnetometers. This paper describes a method for using accelerometer data combined with orientation estimates from the same modules to calculate position during walking and running. The periodic nature of these motions includes short periods of zero foot velocity when the foot is in contact with the ground. This pattern allows for precise drift error correction. Relative position is calculated through double integration of drift corrected accelerometer data. Preliminary experimental results for various types of motion including walking, side stepping, and running document accuracy of distance and position estimates.

Real-time tracking of human body motion is an important technology in synthetic environments, robotics, and other human-computer interaction applications. This paper presents an extended Kalman filter designed for real-time estimation of the orientation of human limb segments. The filter processes data from small inertial/magnetic sensor modules containing triaxial angular rate sensors, accelerometers, and magnetometers. The filter represents rotation using quaternions rather than Euler angles or axis/angle pairs. Preprocessing of the acceleration and magnetometer measurements using the Quest algorithm produces a computed quaternion input for the filter. This preprocessing reduces the dimension of the state vector and makes the measurement equations linear. Real-time implementation and testing results of the quaternion-based Kalman filter are presented. Experimental results validate the filter design, and show the feasibility of using inertial/magnetic sensor modules for real-time human body motion tracking

A human body motion tracking system based on use of the MARG (Magnetic, Angular Rate, and Gravity) sensors has been under development at the Naval Postgraduate School and Miami University. The design of a quaternion-based Kalman filter for processing the MARG sensor data was described in [1]. This paper presents the real-time implementation and testing results of the quaternion-based Kalman filter. Experimental results validate the Kalman filter design, and show the feasibility of the MARG sensors for real-time human body motion tracking.

Rigid body orientation can be estimated in a "sourceless manner" through the use of small three degree of freedom sensor modules containing orthogonally mounted triads of micromachined angular rate sensors, accelerometers, and magnetometers. With proper filter design, drift errors can be eliminated. However, variations in the direction of the local magnetic field reference vector can cause errors in the estimated orientation. The experimental work described in this paper attempts to quantify these errors with an eye toward the development of corrective algorithms. To determine the types and magnitudes of errors that can be expected, three different types of inertial/magnetic sensor modules were subjected to controlled changes in the direction and magnitude of the local magnetic field. The amount of magnetic variation caused by several common objects was also measured in order to gain insight into the magnitude of errors that can be expected during operation in a typical environment. The experiments indicate that variations in the direction of the local magnetic field lead to errors only in azimuth estimation when using inertial/magnetic sensor modules. In a common room environment, errors due to local variations caused by objects such as electrical heaters, CRT monitors, and metal furniture can be expected to be no more than 16 degrees. In most cases these errors can be avoided by maintaining a separation of approximately two feet from the source of interference.

Real-time tracking of human body motion has applications in tele-operation, synthetic reality and others. A motion tracking system based on use of the MARG sensors has been under development at Naval Postgraduate School and Miami University. The magnetic, angular rate, and gravity (MARG) sensor modules use a combination of three orthogonal magnetometers, three orthogonal angular rate sensors, and three orthogonal accelerometers to measure 3-D orientation of individual limb segments in order to determine posture. This paper presents the latest results of the MARG human body motion tracking system. The design and implementation of a control interface unit (CIU), a real-time 3-D human avatar called "Andy", and a concurrent client-server program are discussed. Experimental testing and evaluation of the overall MARG system is also presented. The system is able to track multiple human limbs in real time. The captured human motion data can be visualized over the Internet by multiple clients using the 3-D avatar.

This presents the latest design and implementation of the magnetic, angular rate, and gravity (MARG) sensor module. The MARG sensor module is designed for measuring 3-DOF orientations in real time without singularities. Each MARG sensor contains orthogonally mounted triads of micromachined rate sensors, accelerometers, and magnetometers for a total of nine sensor components. With an integrated microcontroller, the overall factor is less than one cubic inch. Digital data output rate is 100 Hz. To simplify calibration procedures and filtering algorithms, it is important that the response of the individual sensor components is linear within the typical operating regions. Experiments were conducted utilizing a precision tilt table and results indicate that all the sensor components are linear. A simple hand calibration method that requires no specialized equipment is also described. It was validated by experiments that indicate hand calibration produces results that are nearly equivalent to those obtained following precision tilt table calibration.

This paper presents an improved Kalman filter for real-time tracking of human body motions. An earlier version of the filter was presented at IROS 2001. Since then, the filter has been substantially improved. Real-time tracking of rigid body orientation is accomplished using the MARG (magnetic, angular rate, and gravity) sensors. A MARG sensor measures the three-dimensional local magnetic field, three-dimensional angular rate, and three-dimensional acceleration. A Kalman filter is designed to process measurements provided by the MARG sensors, and to produce real-time orientation represented in quaternions. There are many design decisions as related to choice of state vectors, output equations, process model, etc. The filter design presented in this paper utilizes the Gauss-Newton method for parameter optimization in conjunction with Kalman filtering. The use of the Gauss-Newton method, particularly the reduced-order implementation introduced in the paper, significantly simplifies the Kalman filter design, and reduces computational requirements.

Presents an extended Kalman filter for real-time estimation of rigid body orientation using the newly developed MARG (magnetic, angular rate, and gravity) sensors. Each MARG sensor contains a three-axis magnetometer, a three-axis angular rate sensor, and a three-axis accelerometer. The filter represents rotations using quaternions rather than Euler angles, which eliminates the long-standing problem of singularities associated with attitude estimation. A process model for rigid body angular motions and angular rate measurements is defined. The process model converts angular rates into quaternion rates, which are integrated to obtain quaternions. The Gauss-Newton iteration algorithm is utilized to find the best quaternion that relates the measured accelerations and earth magnetic field in the body coordinate frame to calculated values in the earth coordinate frame. The best quaternion is used as part of the measurements for the Kalman filter. As a result of this approach, the measurement equations of the Kalman filter become linear, and the computational requirements are significantly reduced, making it possible to estimate orientation in real time. Extensive testing of the filter with synthetic data and actual sensor data proved it to be satisfactory. Test cases included the presence of large initial errors as well as high noise levels. In all cases the filter was able to converge and accurately track rotational motions.

Rigid body orientation can be determined without the aid of a generated source using nine-axis MARG (Magnetic field, Angular Rate, and Gravity) sensor unit containing three orthogonally mounted angular rate sensors, three orthogonal linear accelerometers and three orthogonal magnetometers. This paper describes a quaternion-based complementary filter algorithm for processing the output data from such a sensor. The filter forms the basis for a system designed to determine the posture of an articulated body in real-time. In the system the orientation relative to an Earth-fixed reference frame of each limb segment is individually determined through the use of an attached MARG sensor. The orientations are used to set the posture of an articulated body model. Details of the fabrication of a prototype MARG sensor are presented. Calibration algorithms for the sensors and the human body model are also presented. Experimental results demonstrate the effectiveness of the tracking system and verify the correctness of the underlying theory.

A small AUV navigation system (SANS) has been developed at the Naval Postgraduate School. The SANS is an integrated GPS/INS navigation system composed of low-cost, small-size components. It is designed to demonstrate the feasibility of using a low-cost inertial measurement unit to navigate between intermittent GPS fixes. This paper reports recent improvements to the SANS hardware, latest testing results after compensating heading-dependent derivations in the TCM-2 compass measurements, and development of an asynchronous Kalman filter for improved position estimation.