Dynamic Surface Reconstruction by Recursive Fusion of Depth and Position Measurements

Surface estimation can be performed based on position or depth measurements. We propose a method to fuse both types of measure-ments. Position measurements are obtained from landmarks on the surface, i.e., they are fixed to a certain point on the surface. In contrast, depth measurements reflect the depth measured along a line emanating from a depth camera and are not fixed to a position on the surface. The proposed approach uses a mixture of Cartesian and polar or spherical coordinate to treat both measurement types accordingly. By doing so, the uncertainties associated with the dif-ferent measurement types are explicitly considered. The presented method represents the surface by a spline and is applicable to both 2D and 3D applications. Surface estimation is considered as a re-cursive filtering problem and standard nonlinear filtering methods such as the unscented Kalman filter can be used to obtain surface estimates. We show a thorough evaluation of the proposed approach in simulations