TY - GEN
T1 - Countering user deviation during redirected walking
AU - Azmandian, Mahdi
AU - Bolas, Mark
AU - Suma, Evan
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Redirected Walking is technique that leverages human perception characteristics to allow locomotion in virtual environments larger than the tracking area. Among the many redirection techniques, some strictly depend on the user's current position and orientation, while more recent algorithms also depend on the user's predicted behavior. This prediction serves as an input to a computationally expensive search to determine an optimal path. The search output is formulated as a series of gains to be applied at different stages along the path. An example prediction could be if a user is walking down a corridor, a natural prediction would be that the user will walk along a straight line down the corridor, and she will choose one of the possible directions with equal probability. In practice, deviations from the expected virtual path are inevitable, and as a result, the real world path traversed will differ from the original prediction. These deviations can not only force the search to select a less optimal path in the next iteration, but also in cases cause the users to go off bounds, requiring resets, causing a jarring experience for the user. We propose a method to account for these deviations by modifying the redirection gains per update frame, aiming to keep the user on the intended predicted physical path.
AB - Redirected Walking is technique that leverages human perception characteristics to allow locomotion in virtual environments larger than the tracking area. Among the many redirection techniques, some strictly depend on the user's current position and orientation, while more recent algorithms also depend on the user's predicted behavior. This prediction serves as an input to a computationally expensive search to determine an optimal path. The search output is formulated as a series of gains to be applied at different stages along the path. An example prediction could be if a user is walking down a corridor, a natural prediction would be that the user will walk along a straight line down the corridor, and she will choose one of the possible directions with equal probability. In practice, deviations from the expected virtual path are inevitable, and as a result, the real world path traversed will differ from the original prediction. These deviations can not only force the search to select a less optimal path in the next iteration, but also in cases cause the users to go off bounds, requiring resets, causing a jarring experience for the user. We propose a method to account for these deviations by modifying the redirection gains per update frame, aiming to keep the user on the intended predicted physical path.
UR - http://www.scopus.com/inward/record.url?scp=84907413753&partnerID=8YFLogxK
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U2 - 10.1145/2628257.2628352
DO - 10.1145/2628257.2628352
M3 - Conference contribution
AN - SCOPUS:84907413753
SN - 9781450330091
T3 - Proceedings of the ACM Symposium on Applied Perception, SAP 2014
BT - Proceedings of the ACM Symposium on Applied Perception, SAP 2014
PB - Association for Computing Machinery
T2 - 11th ACM Symposium on Applied Perception, SAP 2014
Y2 - 8 August 2014 through 9 August 2014
ER -