Surface estimation can be performed based on position or depth measurements. We propose a method to fuse both types of measurements. 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 different 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.

This is an extended version of the paper “Recursive Fusion of Noisy Depth and Position Measurements for Surface Reconstruction” [18] published at the 16th International Conference on In-formation Fusion (Fusion 2013), which received the Jean-Pierre Le Cadre Award for Best Paper.