Quaternion Based Camera Pose Estimation From Matched Feature Points
This provides a more robust solution for computer vision applications like robotics and augmented reality, though it is incremental as it builds on existing pose estimation methods.
The authors tackled the camera pose estimation problem by developing a quaternion-based algorithm that decouples rotation and translation estimation, enabling it to work for all point configurations, including critical surfaces like coplanar points. Their method demonstrated greater robustness to noise and outliers compared to commonly used algorithms in simulations and real-world images.
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.