Camera Ego-Motion Estimation Using Phase Correlation under Planar Motion Constraint

Abstract

An intelligent robotic living assistive system has become a popular research in the last decade. One of the important topics in that research area is 3D object reconstruction from multiple views. This process may depend on motion estimation using vision. However, often a domestic robot on an electric wheel chair has to move in a steep rotational angle that causes motion estimation from vision to become inaccurate. In addition, an oblique viewing angle creates a perspective distortion to the captured images, which further worsens the estimation result. Hence, in this paper, we propose a new approach by altering the motion estimation problem into a 2D image registration problem. Our method’s accuracy is very close to that of the Scale Invariant Feature Transform (SIFT) features tracker, whereas the Kanade-Lucas-Tomasi (KLT) tracker’s drops as soon as the rotational angle reaches about 40¿. Although our method is 2.7 times slower than the KLT tracker, it is 19 times faster than the SIFT tracker.