A PnP Algorithm for Two-Dimensional Pose Estimation
This work addresses pose estimation for wheeled robotics platforms, offering incremental improvements by adapting a known method to a specific constraint.
The paper tackles the problem of 2D pose estimation for cameras on wheeled robots by proposing a PnP algorithm that leverages the 2D motion constraint to improve accuracy and reduce ambiguities, resulting in favorable comparisons to existing 3D PnP algorithms in terms of accuracy, performance, and noise robustness.
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.