The last decade has seen a dramatic expansion in the deployment of Unmanned Airborne Vehicles (UAVs), as witnessed by deployments to Bosnia, Afghanistan and Iraq.  Despite their widespread use in military applications, there are only a handful of operational commercial UAV systems in place, as current systems cannot be certified for use in civilian airspace. In order for UAVs to be integrated into civil airspace, it must be shown that the UAV system has at least an equivalent level of safety to human-piloted platforms.

One of the current barriers of certification to overcome is the inability for a UAV system to autonomously perform a safe forced landing in the event of an emergency.  Damien Dusha of the Airborne Avionics Research Group (AARG) at QUT is part of a research group developing a forced landing system for a UAV using machine vision techniques. In order for a UAV to plan and execute a safe forced landing, it must first develop situational awareness.  One technique the group is focussing on is horizon detection.

Horizon Detection

Existing techniques of horizon detection build upon a number of assumptions:

1. An image may be segmented into sky and non-sky classes
2. The horizon falls on an edge between the sky and non-sky class
3. The horizon is approximated as a straight line
4. The horizon is coplanar to the local tangent plane

Image Processing

The images captured from the flight data are first broken up into red, green and blue channels.  Each channel undergoes morphological smoothing, followed by edge detection. Although the positions of the horizon in each of the edge maps are reasonably well correlated, image noise usually causes few edge pixels to occupy the same position in each of the planes. In addition, the horizon will not be perfectly straight in all but the most benign cases. Therefore, before combining the edge maps, dilation is performed on each map to increase the overlap over the horizon. 

Figure 1: Image from test flight (left), along with edge map from blue channel (centre) and red channel (right). While many of the edges detected in the individual channels do not match, it is possible to detect the horizon in both.

The algorithm devised by AARG has met with some success.  It has been tested in a number of different conditions, such as changing light situations and glare. It has also had some success with noisy video links. Problems have been encountered in situations in which there is a strong line in view, such as the runway seen in figure 4, or in very hazy conditions.

Participants

Damien Dusha, Associate Professor Rodney A. Walker
Airborne Avionics Research Group, Queensland University of Technology