Bayesian Perspective-plane (BPP) for Localization

The "perspective-plane" problem proposed in this paper is similar to the "perspective-n-point (PnP)" or "perspective-n-line (PnL)" problems, yet with broader applications and potentials, since planar scenes are more widely available than control points or lines in practice. We address this problem in the Bayesian framework and propose the "Bayesian perspective-plane (BPP)" algorithm, which can deal with more generalized constraints rather than type-specific ones to determine the plane for localization. Computation of the plane normal is formulated as a maximum likelihood problem, and is solved by using the Maximum Likelihood Searching Model (MLS-M). Two searching modes of 2D and 1D are presented. With the computed normal, the plane distance and the position of the object or camera can be computed readily. The BPP algorithm has been tested with real image data by using different types of scene constraints. The 2D and 1D searching modes were illustrated for plane normal computation. The results demonstrate that the algorithm is accurate and generalized for object localization.