Image-Based Visual Servoing With Unknown Point Feature Correspondence

This paper presents a new image-based visual servoing approach that simultaneously solves the feature correspondence and control problem. Using a finite-time optimal control framework, feature correspondence is implicitly solved for each new image during the control selection, alleviating the need for additional image processing and feature tracking. The proposed approach demonstrates mild robustness properties and leads to acceptable or improved image feature behavior and robot trajectories compared to classical image-based visual servoing, particularly for underactuated robots. As such, preliminary experimental results using a small unmanned quadrotor are also presented.