  Solving Perspective-n-Point (PnP) problems is a traditional way of estimating
object poses. Given outlier-contaminated data, a pose of an object is
calculated with PnP algorithms of n = {3, 4} in the RANSAC-based scheme.
However, the computational complexity considerably increases along with n and
the high complexity imposes a severe strain on devices which should estimate
multiple object poses in real time. In this paper, we propose an efficient
method based on 1-point RANSAC for estimating a pose of an object on the
ground. In the proposed method, a pose is calculated with 1-DoF
parameterization by using a ground object assumption and a 2D object bounding
box as an additional observation, thereby achieving the fastest performance
among the RANSAC-based methods. In addition, since the method suffers from the
errors of the additional information, we propose a hierarchical robust
estimation method for polishing a rough pose estimate and discovering more
inliers in a coarse-to-fine manner. The experiments in synthetic and real-world
datasets demonstrate the superiority of the proposed method.
