# A PnP Algorithm for Two-Dimensional Pose Estimation

arXiv: https://arxiv.org/abs/2312.08488 · HTML: https://mlsanity.com/paper/2312.08488/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [MLPnP - A Real-Time Maximum Likelihood Solution to the Perspective-n-Point Problem](https://mlsanity.com/paper/1607.08112/) (2016)
- [EKFPnP: Extended Kalman Filter for Camera Pose Estimation in a Sequence of Images](https://mlsanity.com/paper/1906.10324/) (2019)

## Abstract (arXiv)

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.