Authors:
Marco A. Chavarria
and
Gerald Sommer
Affiliation:
Cognitive Systems Group. Christian-Albrechts-University of Kiel, Germany
Keyword(s):
Pose estimation, ICP algorithm, monogenic signal, pre-alignment, global and local features.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Methodologies and Methods
;
Model-Based Object Tracking in Image Sequences
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
Abstract:
In this paper we present a new variant of ICP (iterative closest point) algorithm based on local feature correlation. Our approach combines global and local feature information to find better correspondence sets and to use them to compute the 3D pose of the object model even for the case of large displacements between model and image data. For such cases, we propose a 2D alignment in the image plane (rotation plus translation) before the feature extraction process. This has some advantages over the classical methods like better convergence and robustness. Furthermore, it avoids the need of a normal pre-alignment step in 3D. Our approach was tested on synthetical and real-world data to compare the convergence behavior and performance against other versions of the ICP algorithm combined with a classical pre-alignment approach.