A Novel Feedforward Youla Parameterization Method for Avoiding Local Minima in Stereo Image Based Visual Servoing Control

TL;DR

This paper introduces a new control method combining feedforward and Youla parameterization to prevent local minima in stereo image-based visual servoing, improving accuracy and robustness in camera pose estimation.

Contribution

The paper presents a novel feedforward Youla parameterization approach that enhances stereo visual servoing by avoiding local minima and ensuring stable, accurate camera positioning.

Findings

Effective avoidance of local minima demonstrated in simulations

Improved accuracy in camera pose estimation

Enhanced robustness of visual servoing control

Abstract

In robot navigation and manipulation, accurately determining the camera's pose relative to the environment is crucial for effective task execution. In this paper, we systematically prove that this problem corresponds to the Perspective-3-Point (P3P) formulation, where exactly three known 3D points and their corresponding 2D image projections are used to estimate the pose of a stereo camera. In image-based visual servoing (IBVS) control, the system becomes overdetermined, as the 6 degrees of freedom (DoF) of the stereo camera must align with 9 observed 2D features in the scene. When more constraints are imposed than available DoFs, global stability cannot be guaranteed, as the camera may become trapped in a local minimum far from the desired configuration during servoing. To address this issue, we propose a novel control strategy for accurately positioning a calibrated stereo camera. Our approach integrates a feedforward controller with a Youla parameterization-based feedback controller, ensuring robust servoing performance. Through simulations, we demonstrate that our method effectively avoids local minima and enables the camera to reach the desired pose accurately and efficiently.