Pose estimation of quadratic surface using surface fitting technique

A key problem in robotics is the estimation of the location and orientation of objects from surface measurement data. This is termed pose estimation. A fundamental task is the pose estimation of known quadratic surfaces from, possibly noisy, data. A solution for this task facilitates pose estimation for more complex objects. Current algorithms frequently converge to local minima of the performance index and/or pay a high computing cost and/or are sensitive to noise, that are unsuited for online applications because of the intensive computer effort required. The goal is to develop a fast and robust algorithm for pose estimation using range data. Here, pose estimation is carried out using algebraic techniques in a two stage optimization procedure involving least squares estimation, or better the method of instrumental variables, and 3×3 matrix diagonalizations. The procedure leads to zero pose estimation error in the noise free finite data case, and in the case of infinite data with additive white noise