Using a hybrid of silhouette and range templates for real-time pose estimation

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Abstract
Pose estimation is a fundamental problem in machine vision. Silhouette and 3D point matching are two of the many popular methods for attacking this problem. The appearance of a silhouette is highly sensitive to changes in the object's planar (WRT the object plane) location and orientation. Therefore, silhouette matching can be used to accurately measure 3 of the 6 pose parameters. The appearance of a silhouette is less sensitive to DOFs that produce out-of-plane motion and so it enables only rough measurement of these 3 pose parameters. 3D point matching, which employs range data, can be used to accurately determine the 3 out-of-plane pose parameters. However, to recognize specific 3d points, one must typically make strong assumptions about the types of features present on an object's surface. Our goal is to solve the more general pose problem, where specific types of features cannot be relied upon because they might not be present. This paper first introduces a novel approach to silhouette matching which employs binary range maps and statistically generated templates. It then describes a hybrid method for template-based pose estimation that leverages silhouette matching for 3 of 6 pose parameters. The remaining 3 parameters are determined using range measurements that do not require the presence of specific features or artifacts. This approach provides a high degree of precision in all 6 DOFs, yet its computational efficiency enables real-time performance. Results from dexterous robot (Robonaut) experiments, using this pose algorithm, are discussed.

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