Trifocal tensor-based 6-DOF visual servoing

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.

Vision-aided localization and navigation based on trifocal tensor geometry

In this paper, a new method for vision-aided navigation based on trifocal tensor geometry is presented. The main goal of the proposed method is to estimate the position of vehicles in global positioning system-denied environments, using a standard inertial navigation system and only a single camera. The geometric trifocal tensor relationship between three images is used as measurement information from the camera, and the primary contribution of this work is the derivation of a measurement model that is able to express the geometric constraints of the trifocal tensor in the global frame. This measurement model does not require including the three-dimensional feature positions in the state vector. In other words, the proposed method does not entail reconstructing the environment. Rather, the method only considers the vehicle state. The vision-aided inertial navigation algorithm that we propose has computational complexity only with regard to the number of features at the current time, and the algorithm is capable of estimating the pose in real environments. Experiments were conducted to show the effectiveness of the proposed method in simulations and real environments.