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 terms of accuracy, performance, and robustness to noise.

翻译：我们提出了一种适用于相机受限于二维运动场景的PnP算法（例如可应用于许多轮式机器人平台）。利用这一假设，由于搜索空间维度的降低，该算法相比三维PnP算法在性能上有显著提升。同时，该方法还减少了模糊位姿估计的发生概率（因为在多数情况下，虚假解会落在运动平面之外）。我们的算法通过几何准则获得近似解，并通过迭代方式优化预测结果。我们将该算法与现有三维PnP算法在精度、性能及噪声鲁棒性方面进行了比较。