We propose a PnP algorithm for a camera constrained to two-dimensional
movement (applicable, for instance, to many wheeled robotics platforms).
Leveraging this assumption allows performance improvements over 3D PnP
algorithms due to the reduction in search space dimensionality. It also reduces
the incidence of ambiguous pose estimates (as, in most cases, the spurious
solutions fall outside the plane of movement). Our algorithm finds an
approximate solution using geometric criteria and refines its prediction
iteratively. We compare this algorithm to existing 3D PnP algorithms in the
cases of general and coplanar point configurations.

我们提出了一种应用于二维运动的相机受限的 PnP 算法，通过降低搜索空间的维度，提高了性能，并减少了姿态估计的模糊度。该算法使用几何标准找到一个近似解，并通过迭代进行预测的优化。我们将该算法与现有的三维 PnP 算法在一般和共面点配置的情况下进行了比较。

二维姿态估计的 PnP 算法

PnP for Two-Dimensional Pose Estimation

Object pose estimation is a fundamental computer vision task exploited in
several robotics and augmented reality applications. Many established
approaches rely on predicting 2D-3D keypoint correspondences using RANSAC
(Random sample consensus) and estimating the object pose using the PnP
(Perspective-n-Point) algorithm. Being RANSAC non-differentiable,
correspondences cannot be directly learned in an end-to-end fashion. In this
paper, we address the stereo image-based object pose estimation problem by (i)
introducing a differentiable RANSAC layer into a well-known monocular pose
estimation network; (ii) exploiting an uncertainty-driven multi-view PnP solver
which can fuse information from multiple views. We evaluate our approach on a
challenging public stereo object pose estimation dataset, yielding
state-of-the-art results against other recent approaches. Furthermore, in our
ablation study, we show that the differentiable RANSAC layer plays a
significant role in the accuracy of the proposed method. We release with this
paper the open-source implementation of our method.

本文介绍了一种使用可微分 RANSAC 层和基于不确定性的多视图 PnP 求解器对基于立体图像的物体姿态估计问题进行解决的方法，并在公开数据集上进行了评估，取得了与其他最近方法相比的最新结果。