Name
Abstract | ||
|---|---|---|
ABSTRACT Accurate drone positioning is challenging because pilots only have a limited position and direction perception of a flying drone from their perspective. This makes conventional joystick-based speed control inaccurate and more complicated and significantly degrades piloting performance. We propose PinpointFly, an egocentric drone interface that allows pilots to arbitrarily position and rotate a drone using position-control direct interactions on a see-through mobile AR where the drone position and direction are visualized with a virtual cast shadow (i.e., the drone’s orthogonal projection onto the floor). Pilots can point to the next position or draw the drone’s flight trajectory by manipulating the virtual cast shadow and the direction/height slider bar on the touchscreen. We design and implement a prototype of PinpointFly for indoor and visual line of sight scenarios, which are comprised of real-time and predefined motion-control techniques. We conduct two user studies with simple positioning and inspection tasks. Our results demonstrate that PinpointFly makes the drone positioning and inspection operations faster, more accurate, simpler and fewer workload than a conventional joystick interface with a speed-control method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3411764.3445110 | Conference on Human Factors in Computing Systems |
Keywords | DocType | Citations |
Visualization, Spatial Perception, Positioning, Videography | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Linfeng Chen | 1 | 8 | 1.88 |
| Kazuki Takashima | 2 | 180 | 29.89 |
| Kazuyuki Fujita | 3 | 34 | 16.15 |
| Yoshifumi Kitamura | 4 | 1040 | 117.66 |