Plane estimation by active vision from point features and image moments

In this paper we experimentally validate and compare three different methods for estimating the 3D parameters of a planar scene from a (possibly time-varying) set of feature points acquired by a moving monocular camera. The first method, based on the classical decomposition of the homography matrix, is meant to serve as a baseline condition classically used in many previous works. The other two methods exploit an active Structure from Motion (SfM) scheme for either extracting the plane from the reconstructed 3D position of all the tracked points, or for directly estimating the plane parameters by considering a set of discrete image moments as visual input. The possible loss/gain of point features during the camera motion is considered in all three methods by, in particular, introducing a suitable weighting strategy for the image moment case. Finally, the results of an experimental validation are presented with a comparative discussion of the pros/cons of the three methods.