Trifocal tensor-based 6-DOF visual servoing

Abstract

This paper proposes a trifocal tensor-based approach for six-degree-of-freedom visual servoing. The trifocal tensor model among the current, desired, and reference views is constructed to describe the geometric relationship of the system. More precisely, to ensure the computation consistency of trifocal tensor, a virtual reference view is introduced by exploiting the transfer relationships between the initial and desired images. Instead of resorting to explicit estimation of the camera pose, a set of visual features with satisfactory decoupling properties are constructed from the tensor elements. Based on the selected features, a visual controller is developed to regulate the camera to a desired pose, and an adaptive update law is used to compensate for the unknown distance scale factor. Furthermore, the system stability is analyzed via Lyapunov-based techniques, showing that the proposed controller can achieve almost global asymptotic stability. Both simulation and experimental results are provided to demonstrate the effectiveness and robustness of our approach under different conditions and case studies.

2.5D Vision-Based Estimation

“…A finite number of simulations or experiments cannot guarantee the global convergence. Zhang et al (2019) make a good demonstration of investigating the state space and analyzing the existence of multiple equilibriums. This helps in figuring out whether the global convergence holds or what the influence of those undesired equilibriums is.…”