Minimal solvers for generalized pose and scale estimation from two rays and one point

Abstract

Estimating the poses of a moving camera with respect to a known 3D map is a key problem in robotics and Augmented Reality applications. Instead of solving for each pose individually, the trajectory can be considered as a generalized camera. Thus, all poses can be jointly estimated by solving a generalized PnP (gPnP) problem. In this paper, we show that the gPnP problem for camera trajectories permits an extremely efficient minimal solution when exploiting the fact that pose tracking allows us to locally triangulate 3D points. We present a problem formulation based on one point-point and two point-ray correspondences that encompasses both the case where the scale of the trajectory is known and where it is unknown. Our formulation leads to closed-form solutions that are orders of magnitude faster to compute than the current stateof- the-art, while resulting in a similar or better pose accuracy.