Obstacle avoidance during walking in real and virtual environments

Fink, Philip W.; Foo, Patrick; Warren, William H.

doi:10.1145/1227134.1227136

Cited by 109 publications

(102 citation statements)

References 29 publications

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“…By using standard models of human locomotion [1,2], it is possible to obtain an expected reference path to each of the targets. Once the user starts moving, the observed movement can be compared to the simulated reference trajectories and a prediction can be made.…”

Section: Related Workmentioning

confidence: 99%

“…From the gathered data , we calculate P(G ∈ G i |τ i ) (see Table 1) and determine the probability as defined in equation (2).…”

Section: Eye Tracking Realizationmentioning

confidence: 99%

“…They propose to use a model of human locomotion to generate a set of reference trajectories to all possible targets the moment the user starts to walk and then compare the observed trajectory to them while the user walks. In the following evaluation the models by Arechavaleta et al [1] and Fink et al [2] are used to generate these reference trajectories. In addition, they presented different schemes for comparing the observed path to the reference trajectories, the most promising ones are based on Dynamic Time Warping (DTW) and the cost-based method.…”

Section: Position Based Predictionmentioning

confidence: 99%

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Eye tracking for locomotion prediction in redirected walking

Zank

Kunz

2016

2016 IEEE Symposium on 3D User Interfaces (3DUI)

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Model predictive control was shown to be a powerful tool for Redirected Walking when used to plan and select future redirection techniques. However, to use it effectively, a good prediction of the user's future actions is crucial. Traditionally, this prediction is made based on the user's position or current direction of movement. In the area of cognitive sciences however, it was shown that a person's gaze can also be highly indicative of his intention in both selection and navigation tasks.In this paper, this effect is used the first time to predict a user's locomotion target during goal-directed locomotion in an immersive virtual environment. After discussing the general implications and challenges of using eye tracking for prediction in a locomotion context, we propose a prediction method for a user's intended locomotion target. This approach is then compared with position based approaches in terms of prediction time and accuracy based on data gathered in an experiment.The results show that, in certain situations, eye tracking allows an earlier prediction compared approaches currently used for redirected walking. However, other recently published prediction methods that are based on the user's position perform almost as well as the eye tracking based approaches presented in this paper.

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Section: Related Workmentioning

confidence: 99%

“…From the gathered data , we calculate P(G ∈ G i |τ i ) (see Table 1) and determine the probability as defined in equation (2).…”

Section: Eye Tracking Realizationmentioning

confidence: 99%

Section: Position Based Predictionmentioning

confidence: 99%

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Eye tracking for locomotion prediction in redirected walking

Zank

Kunz

2016

2016 IEEE Symposium on 3D User Interfaces (3DUI)

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“…E.g. [4,5] present dynamic walking models which can generate natural human walking trajectories numerically. Hence, we designed a VE that constrained the walk paths 1 www.oculusvr.com 2 www.unity3d.com so that the trajectories could be predefined.…”

Section: Virtual Environmentmentioning

confidence: 99%

Planning redirection techniques for optimal free walking experience using model predictive control

Nescher

Huang

Kunz

2014

2014 IEEE Symposium on 3D User Interfaces (3DUI)

121

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Redirected Walking (RDW) is a technique that allows exploring immersive virtual environments by real walking in a small physical room. RDW employs so-called redirection techniques (RETs) to manipulate the user's real world trajectory in such a way that he remains within the boundaries of the physical room. Different RETs were suggested and evaluated in the past. In addition, steering algorithms were proposed that apply a limited set of RETs to redirect a user away from the physical room's boundaries.Within this paper, a generalized approach to planning and applying RETs is presented. It is capable of dynamically selecting suitable RETs and also controlling parameters like their strengths. The problem of steering a user in a small physical room using RETs is formulated as an optimal control problem. This allows applying an efficient probabilistic planning algorithm to maximize the free walking experience. The proposed algorithm uses a map of the virtual environment to continuously determine the optimal RET that has to be applied next.The suggested algorithm is evaluated within a user study and compared to a state-of-the-art steering algorithm. Results show that for the given virtual environment, it is able to reduce the number of collisions with the room boundaries by 41% and furthermore reduces the amount of applied redirections significantly.

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“…As in the real world, depth spans are often compressed relative to frontal spans [10]. Additionally, Fink et al [5] showed differences in trajectories between real and virtual movements, indicating greater uncertainty about object positions.…”

Section: Distance Perception In Virtual Environmentsmentioning

confidence: 99%

Virtual Distance Estimation in a CAVE

Marsh¹,

Chardonnet²,

Merienne³

2014

Spatial Cognition IX

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract. Past studies have shown consistent underestimation of distances in virtual reality, though the exact causes remain unclear. Many virtual distance cues have been investigated, but past work has failed to account for the possible addition of cues from the physical environment. We describe two studies that assess users' performance and strategies when judging horizontal and vertical distances in a CAVE. Results indicate that users attempt to leverage cues from the physical environment when available and, if allowed, use a locomotion interface to move the virtual viewpoint to facilitate this.

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Obstacle avoidance during walking in real and virtual environments

Cited by 109 publications

References 29 publications

Eye tracking for locomotion prediction in redirected walking

Eye tracking for locomotion prediction in redirected walking

Planning redirection techniques for optimal free walking experience using model predictive control

Virtual Distance Estimation in a CAVE

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