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.

翻译：本文提出一种针对相机在二维平面内运动场景的PnP算法（适用于如轮式机器人平台等场景）。利用该平面运动约束可降低搜索空间维度，从而在三维PnP算法基础上实现性能提升。由于虚假解通常位于运动平面之外，该算法还能显著降低位姿估计的歧义性。本算法通过几何准则获得近似解，并通过迭代优化实现预测精度的持续提升。我们分别针对一般点云配置与共面点配置两种场景，将本算法与现有三维PnP算法进行对比验证。