Camera drones, a rapidly emerging technology, offer people the ability to remotely inspect an environment with a high degree of mobility and agility. However, manual remote piloting of a drone is prone to errors. In contrast, autopilot systems can require a significant degree of environmental knowledge and are not necessarily designed to support flexible visual inspections. Inspired by camera manipulation techniques in interactive graphics, we designed StarHopper, a novel touch screen interface for efficient object-centric camera drone navigation, in which a user directly specifies the navigation of a drone camera relative to a specified object of interest. The system relies on minimal environmental information and combines both manual and automated control mechanisms to give users the freedom to remotely explore an environment with efficiency and accuracy. A lab study shows that StarHopper offers an efficiency gain of 35.4% over manual piloting, complimented by an overall user preference towards our object-centric navigation system.
StarHopper is a novel touch screen interface for efficient and flexible object-centric camera drone navigation The authors outline a new drone control interface StarHopper that they have developed, combining automated and manual piloting into a new hybrid navigation interface and gets rid of the assumption that the target object is already in the drone’s FOV by using an additional overhead camera. This paper presents StarHopper, a system for semi-automatic drone navigation in the context of remote inspection. Introduces StarHopper, an application that uses computer vision techniques with touch input to support drone piloting with an object-centric approach.