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Robust Aerial Object Tracking in High Dynamic Flight Maneuvers

A. Nussberger, H. Grabner, L. Van Gool
Proc. of International Conference on Unmanned Aerial Vehicles in Geomatics
Toronto, ON, August 2015


Integrating drones into the civil airspace is one of the biggest challenges for civil aviation, responsible authorities and involved companies around the world in the upcoming years. For a full integration into non-segregated airspace such a system has to provide the capability to automatically detect and avoid other airspace users. Electro-optical cameras have proven to be an adequate sensor to detect all types of aerial objects, especially for smaller ones such as gliders or paragliders. Robust detection and tracking of approaching traffic on a potential collision course is the key component for a successful avoidance maneuver. In this paper we focus on the aerial object tracking during dynamic flight maneuvers of the own-ship where accurate attitude information corresponding to the camera images is essential. Because the 'detect and avoid' functionality typically extends existing autopilot systems the received attitude measurements have unknown delays and dynamics. We present an efficient method to calculate the angular rates from a multi camera rig which we fuse with the delayed attitude measurements. This allows for estimating accurate absolute attitude angles for every camera frame. The proposed method is further integrated into an aerial object tracking framework. A detailed evaluation of the pipeline on real collision encounter scenarios shows that the multi camera rig based attitude estimation enables the correct tracking of approaching traffic during dynamic flight, at which the tracking framework previously failed.

Link to publisher's page
  author = {A. Nussberger and H. Grabner and L. Van Gool},
  title = {Robust Aerial Object Tracking in High Dynamic Flight Maneuvers},
  booktitle = {Proc. of International Conference on Unmanned Aerial Vehicles in Geomatics},
  year = {2015},
  month = {August},
  publisher = {ISPRS},
  keywords = {}