Multi-scale object representation and localization using range data

This thesis examines an approach to the representation of 3-D objects based on multi-scale surface patches. These patches are computed using multiple alternative decompositions of the surface based on the signs of the mean and Gaussian curvatures. The approach is applicable both to polyhedral objects as well as smoothly curved objects.A hierarchical ranking of these patches is then used to describe individual objects based on geometric information. These geometric descriptors are ranked according to several criteria expressing their estimated stability and utility.Pose estimation is cast as an optimal matching problem. The geometric pose transformation between two views of a simple curved object is found by matching multi-scale descriptions corresponding to the two views. Different combinations of possible three patch correspondences are found and ranked between the two views and the position transformation (rotation and translation) is computed. The starting patches are constrained to be those with the most stable description. The cost of matching the two sets of representative patches based on the position transformation is computed. The final pose estimate is obtained from the correspondence that produces the best global consistency.The algorithm's applicability to pose estimation is demonstrated by examples using real range data and its behaviour in the presence of noise is validated. Its use in object recognition is then discussed