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

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.