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Conference Papers Year : 2018

Visual Servoing With Trifocal Tensor

Jian Chen

Abstract

In this paper, a trifocal tensor-based approach is developed for 6 degrees-of-freedom visual servoing. The trifocal tensor model among the current, desired, and initial views is introduced to describe the geometric relationship. Then, the tensor elements are refined to construct the visual feedback without resorting to explicit estimation of the camera pose. Based on the extracted tensor features, an adaptive controller is designed to drive the camera to a desired pose and compensate for the unknown distance scale factor. Moreover, Lyapunov-based techniques are exploited to analyze the system stability and convergence domain. Simulation results are provided to demonstrate the effectiveness of the developed approach.
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Dates and versions

hal-01863424 , version 1 (28-08-2018)

Identifiers

  • HAL Id : hal-01863424 , version 1

Cite

Kaixiang Zhang, Jian Chen, François Chaumette. Visual Servoing With Trifocal Tensor. CDC'18 - 57th IEEE Conference on Decision and Control, Dec 2018, Miami Beach, United States. pp.1-7. ⟨hal-01863424⟩
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