3D object recognition using deformable model for negating sensing error

Focusing on 3D object recognition for handling-robot tasks, we developed a registration method for point data measured from a real object and model surfaces. On the basis of the iterative-closest-point (ICP) algorithm, we proposed a registration technique that deforms model shapes instead of correcting measured range data including distance errors. We call our technique a "viewpoint-dependent remodeling ICP" algorithm. Even when a laser range finder only is used, this technique can reduce the effects of errors depending on surface characteristics such as colors and reflectance properties. In the preliminary stages, the relationships between distance errors and surface characteristics of points on object surfaces are determined and added to the models. In object recognition stages, we measure point data, and do registration while changing the model position and attitude and deforming the model shape. The deformation depends on the relationships and the relative positions of the model surfaces and the sensor position. In preliminary experimental tests, we measured distances to black and white papers and evaluated the distance errors. Moreover, we simulated recognizing the bottle covered with these papers. In this simulation, it was verified that our technique has convergence and improves accuracy of correspondence estimations between measured data and models.