Countering user deviation during redirected walking

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.