Position and orientation determination using quadric fitting optimization for object’s motion analyzing

摘要

Abstract The accurate determination of the position and orientation of moving objects from images is a critical challenge in visual sensing, with significant implications for robotics, augmented reality and computer vision. Traditional pose estimation methods often fail in complex scenarios due to inconsistent and imprecise feature extraction. This research introduces a novel quadric fitting optimization framework that exploits the compact representation of ellipsoids for robust motion analysis. We propose Manhattan and Euclidean norm-based solvers, designed to optimize quadric fitting for varying object scales and distances. A hybrid approach is developed to strategically fuse these solvers to maximize accuracy and efficiency in various practical applications. Theoretical analysis and extensive experimental validation demonstrate the significant improvements offered by our method over conventional techniques, particularly under challenging conditions. This work not only advances the state of the art in object pose determination, but also provides a solid foundation for improved manipulation and understanding of moving objects in dynamic environments.