We propose a PnP algorithm for a camera constrained to two-dimensional motion (applicable, for instance, to many wheeled robotics platforms). Leveraging this assumption allows accuracy and 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 by solving a polynomial system and refines its prediction iteratively to minimize the reprojection error. The algorithm compares favorably to existing 3D PnP algorithms in terms of accuracy, performance, and robustness to noise.

翻译：我们提出了一种针对受限于二维运动的相机（适用于许多轮式机器人平台）的PnP算法。利用这一假设，通过降低搜索空间维度，相较于3D PnP算法可在精度和性能上获得提升。此外，该算法还可降低模糊位姿估计的出现概率（因为在多数情况下，伪解会落在运动平面之外）。通过求解多项式系统获得近似解，并迭代优化预测以最小化重投影误差。实验表明，该算法在精度、性能及鲁棒性方面均优于现有3D PnP算法。