LAUSR

This paper presents a method for coarse alignment of point clouds by introducing a new descriptor based on the local curvature. The method is developed for model fitting a CAD model for use in robotic assembly. The method is based on selecting keypoints depending on shape factors calculated from the local covariance matrix of the surface. A descriptor is then calculated for each keypoint by fitting two spheres that describe the curvature of the surface. The spheres are calculated using conformal geometric algebra, which gives a convenient and efficient description of the geometry. The keypoint descriptors for the model and the observed point cloud are then compared to estimate the corresponding keypoints, which are used to calculate the displacement. The method is tested in several experiments. One experiment is for robotic assembly, where objects are placed on a table and their position and orientation is estimated using a 3-D CAD model. Note to Practitioners— 3-D cameras can be used in robotic assembly for recognizing objects, and for determining the position and orientation of parts to be assembled. In such applications, 3-D CAD models will be available for the objects, and point clouds representing each object can be generated for comparison with the observed point clouds from the 3-D camera. It is not straightforward to use existing descriptors in this paper, as the point cloud from the CAD model and the observed point cloud may differ due to different viewpoints and potential occlusions. The method proposed in this paper is intended to be easy to apply to industrial assembly problems where there is a need for a robust estimation of the displacement of an object, either as a coarse estimate for use in grasping or as an initial guess to use in fine registration for demanding assembly operations with close tolerances. The method exploits the curvature of the point clouds to accurately describe the surrounding surface of each point. This method serves as a basis for future industrial implementations.