Augmented Reality (AR) benefits telementoring by enhancing the communication between the mentee and the remote mentor with mentor authored graphical annotations that are directly integrated into the mentee’s view of the workspace. An important problem is conveying the workspace to the mentor effectively, such that they can provide adequate guidance. AR headsets now incorporate a frontfacing video camera, which can be used to acquire the workspace. However, simply providing to the mentor this video acquired from the mentee’s first-person view is inadequate. As the mentee moves their head, the mentor’s visualization of the workspace changes frequently, unexpectedly, and substantially. This paper presents a method for robust high-level stabilization of a mentee first-person video to provide effective workspace visualization to a remote mentor. The visualization is stable, complete, up to date, continuous, distortion free, and rendered from the mentee’s typical viewpoint, as needed to best inform the mentor of the current state of the workspace. In one study, the stabilized visualization had significant advantages over unstabilized visualization, in the context of three number matching tasks. In a second study, stabilization showed good results, in the context of surgical telementoring, specifically for cricothyroidotomy training in austere settings.

This poster presents the design, implementation, and evaluation of a method for robust high-level stabilization of mentees first-person video in augmented reality (AR) telementoring. This video is captured by the front-facing built-in camera of an AR headset and stabilized by rendering from a stationary view a planar proxy of the workspace projectively texture mapped with the video feed. The result is stable, complete, up to date, continuous, distortion free, and rendered from the mentee's default viewpoint. The stabilization method was evaluated in two user studies, in the context of number matching and for cricothyroidotomy training, respectively. Both showed a significant advantage of our method compared with unstabilized visualization.