Consistent and Optimal Solution to Camera Motion Estimation

A novel approach for camera motion estimation that achieves asymptotic Gaussianity in noise distribution through special error construction for essential matrix estimation. The method enables bias elimination through variance estimation and achieves maximum likelihood optimal estimation via single-step Gauss-Newton iteration, reaching the Cramér-Rao lower bound.

My Contributions: Designed the testing framework for generating synthetic data and evaluating epipolar geometry. Implemented state-of-the-art relative pose estimation algorithms in C++ and adapted learning-based methods for comparative evaluation.