Pose-estimation-based visual servoing for differential-drive robots using the 1D trifocal tensor

A pose-estimation-based approach to perform visual control for differential-drive robots is presented in this paper. Our scheme recovers the camera location (position and orientation) using an extended kalman filter (EKF) algorithm with the 1D trifocal tensor (TT) as measurement model. This new visual servoing scheme allows knowing the real world path performed by the robot without the computational load introduced by position-based approaches. A state-estimated feedback control law is designed to solve a tracking problem for the lateral and longitudinal robot coordinates. The desired trajectories to be tracked ensure total correction of both position and orientation using a single control law, even though the orientation is a DOF in the control system. The effectiveness of our approach is tested via simulations.