# Quaternion Based Camera Pose Estimation From Matched Feature Points

arXiv: https://arxiv.org/abs/1704.02672 · HTML: https://mlsanity.com/paper/1704.02672/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2017.

## Built on by (most cited first, 5 of 5)

- [Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares](https://mlsanity.com/paper/2305.09527/) (2023)
- [The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions](https://mlsanity.com/paper/2204.02256/) (2022)
- [Six-Point Method for Multi-Camera Systems with Reduced Solution Space](https://mlsanity.com/paper/2402.18066/) (2024)
- [Robust 360-8PA: Redesigning The Normalized 8-point Algorithm for 360-FoV Images](https://mlsanity.com/paper/2104.10900/) (2021)
- [On Relative Pose Recovery for Multi-Camera Systems](https://mlsanity.com/paper/2102.11996/) (2021)

## Abstract (arXiv)

We present a novel solution to the camera pose estimation problem, where rotation and translation of a camera between two views are estimated from matched feature points in the images. The camera pose estimation problem is traditionally solved via algorithms that are based on the essential matrix or the Euclidean homography. With six or more feature points in general positions in the space, essential matrix based algorithms can recover a unique solution. However, such algorithms fail when points are on critical surfaces (e.g., coplanar points) and homography should be used instead. By formulating the problem in quaternions and decoupling the rotation and translation estimation, our proposed algorithm works for all point configurations. Using both simulated and real world images, we compare the estimation accuracy of our algorithm with some of the most commonly used algorithms. Our method is shown to be more robust to noise and outliers. For the benefit of community, we have made the implementation of our algorithm available online and free.