Recent research has proposed teleoperation of robotic and aerial vehicles using head motion tracked by a head-mounted display (HMD). First-person views of the vehicles are usually captured by onboard cameras and presented to users through the display panels of HMDs. This provides users with a direct, immersive and intuitive interface for viewing and control. However, a typically overlooked factor in such designs is the latency introduced by the vehicle dynamics. As head motion is coupled with visual updates in such applications, visual and control latency always exists between the issue of control commands by head movements and the visual feedback received at the completion of the attitude adjustment. This causes a discrepancy between the intended motion, the vestibular cue and the visual cue and may potentially result in simulator sickness. No research has been conducted on how various levels of visual and control latency introduced by dynamics in robots or aerial vehicles affect users' performance and the degree of simulator sickness elicited. Thus, it is uncertain how much performance is degraded by latency and whether such designs are comfortable from the perspective of users. To address these issues, we studied a prototyped scenario of a head motion controlled quadcopter using an HMD. We present a virtual reality (VR) paradigm to systematically assess the effects of visual and control latency in simulated drone control scenarios.

We present a method for estimating the Motion-to-Photon (End-to-End) latency of head mounted displays (HMDs). The specific HMD evaluated in our study was the Oculus Rift DK2, but the procedure is general. We mounted the HMD on a pendulum to introduce damped sinusoidal motion to the HMD during the pendulum swing. The latency was estimated by calculating the phase shift between the captured signals of the physical motion of the HMD and a motion-dependent gradient stimulus rendered on the display. We used the proposed method to estimate both rotational and translational Motion-to-Photon latencies of the Oculus Rift DK2.

Superimposed luminance noise is typical of imagery from devices used for low-light vision such as image intensifiers (i.e., night vision devices). In four experiments, we measured the ability to detect and discriminate motion-defined forms as a function of stimulus signal-to-noise ratio at a variety of stimulus speeds. For each trial, observers were shown a pair of image sequences - one containing dots in a central motion-defined target region that moves coherently against the surrounding dots, which moved in the opposite or in random directions, while the other sequence had the same random/uniform motion in both the center and surrounding parts. They indicated which interval contained the target stimulus in a two-interval forced-choice procedure. In the first experiment, simulated night vision images were presented with Poisson-distributed spatiotemporal image noise added to both the target and surrounding regions of the display. As the power of spatiotemporal noise was increased, it became harder for observers to detect the target, particularly at the lowest and highest dot speeds. The second experiment confirmed that these effects also occurred with low illumination in real night vision device imagery, a situation that produces similar image noise. The third experiment demonstrated that these effects generalized to Gaussian noise distributions and noise created by spatiotemporal decorrelation. In the fourth experiment, we found similar speed-dependent effects of luminance noise for the discrimination (as opposed to detection) of the shape of a motion-defined form. The results are discussed in terms of physiological motion processing and for the usability of enhanced vision displays under noisy conditions.