A Theory of Single-Viewpoint Catadioptric Image Formation
International Journal of Computer Vision
Catadioptric Projective Geometry
International Journal of Computer Vision
Epipolar Geometry for Central Catadioptric Cameras
International Journal of Computer Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
The Radial Trifocal Tensor: A Tool for Calibrating the Radial Distortion of Wide-Angle Cameras
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
From omnidirectional images to hierarchical localization
Robotics and Autonomous Systems
Image-based Visual Servoing with Central Catadioptric Cameras
International Journal of Robotics Research
Switching visual control based on epipoles for mobile robots
Robotics and Autonomous Systems
Real-time monocular visual odometry for on-road vehicles with 1-point RANSAC
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
A novel 1D trifocal tensor-based control for differential-drive robots
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Visual control through the trifocal tensor for nonholonomic robots
Robotics and Autonomous Systems
Localization and Matching Using the Planar Trifocal Tensor With Bearing-Only Data
IEEE Transactions on Robotics
Photometric visual servoing for omnidirectional cameras
Autonomous Robots
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The precise positioning of robotic systems is of great interest particularly in mobile robots. In this context, the use of omnidirectional vision provides many advantages thanks to its wide field of view. This paper presents an image-based visual control to drive a mobile robot to a desired location, which is specified by a target image previously acquired. It exploits the properties of omnidirectional images to preserve the bearing information by using a 1D trifocal tensor. The main contribution of the paper is that the elements of the tensor are introduced directly in the control law and neither any a priori knowledge of the scene nor any auxiliary image are required. Our approach can be applied with any visual sensor obeying approximately a central projection model, presents good robustness to image noise, and avoids the problem of a short baseline by exploiting the information of three views. A sliding mode control law in a square system ensures stability and robustness for the closed loop. The good performance of the control system is proven via simulations and real world experiments with a hypercatadioptric imaging system.