Stereo Vision based Relative Pose and Motion Estimation for Unmanned Helicopter Landing

Abstract

In this paper, we present a real-time stereo vision based pose and motion estimation system that will be used for landing an unmanned helicopter on a moving target such as a ship deck. The vision algorithm mainly consists of a feature extraction task and a pose and motion estimation task. The 2D planer target with regular features defined can significantly simplify the feature extraction task such as corner detection and feature points matching. To effectively estimate the distance between the camera carrier and target a stereo camera system is applied. By means of sub-pixel corner location the precisions of pose estimation and relative motion detection can be improved. We present results from semi-physical simulation which show that our vision algorithm is accurate and robust. The methodology provides an effective subsystem for the development of autonomous robot helicopter that will land on a given target under the guide of vision.