On-board real-time pose estimation for UAVs using deformable visual contour registration

Abstract

We present a real time method for pose estimation of objects from an UAV, using visual marks placed on non planar surfaces. It is designed to overcome constraints in small aerial robots, such as slow CPUs, low resolution cameras and image deformations due to distortions introduced by the lens or by the viewpoint changes produced during the flight navigation. The method consists of shape registration from extracted contours in an image. Instead of working with dense image patches or corresponding image features, we optimize a geometric alignment cost computed directly from the raw polygonal representations of the observed regions using efficient clipping algorithms. Moreover, instead of doing 2D image processing operations, the optimization is performed in the polygon representation space, allowing real-time projective matching. Deformation modes are easily included in the optimization scheme, allowing an accurate registration of different markers attached to curved surfaces using a single deformable prototype. As a result, the method achieves accurate object pose estimation precision in real-time, which is very important for interactive UAV tasks, for example for short distance surveillance or bar assembly. We describe the main algorithmic components of the method and present experiments where our method yields an average error of less than 5mm in position at a distance of 0.7m, using a visual mark of 19mm x 19mm. Finally, we compare these results with current computer vision state-of-the-art systems.