Surface Recognition by Registering Data Curves from Touch

Model-based recognition of an object typically involves matching dense 3D range data. The computational cost is directly affected by the amount of data of which a transformation needs to be found before carrying out the match against a model. This paper investigates recognition using “one-dimensional” data, more specifically, points sampled along three concurrent curves on the surface of an object. The introduced method determines the quality of match against a model in two steps. First, the Gaussian and mean curvatures at the curve intersection point are estimated and used in a table lookup to find multiple candidate points on the model that have similar local geometry. Second, starting at each point, local optimization is conducted to search for a possible location of the curve intersection on the model as well as an orientation that leads to a good match of all data points. The best match between the model and the data curves is chosen over the results obtained from all candi...

• Type: Conference
• Year: 2006
• Where: IROS
• Authors: Rinat Ibrayev, Yan-Bin Jia
• Keywords: Candidate Points, Curve Intersection, IROS 2006, Match, Robotics