Obstacle detection technique to solve poor texture appearance of the obstacle by categorising image's region using cues from expansion of feature points for small UAV

Abstract

Achieving a reliable obstacle detection system for small unmanned aerial vehicle (UAV) is very challenging due to its size and weight constraints. Prior works tend to employ the vision sensor as main detection sensor but resulting to high dependency on texture appearance while not having distance sensing capabilities. Besides, most of wide spectrum range sensors are heavy and expensive. The contribution of this work is on different based-sensor integration technique to increase reliability of detection. A method was developed to create trusted avoidance path by categorising the region in environment into two regions, which are the obstacle region and free region. Cues from expansion of the features points are used to extract the depth information of the environment and classify the region in the image frame. The results show that the proposed system able to handle multiple obstacle and create safe path regardless of the texture and size of the obstacle.