@conference {14207,
	title = {Combining motion from texture and lines for visual navigation},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007. IROS 2007},
	year = {2007},
	month = {2007/11/29/Oct. },
	pages = {232 - 239},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Two novel methods for computing 3D structure information from video for a piecewise planar scene are presented. The first method is based on a new line constraint, which clearly separates the estimation of distance from the estimation of slant. The second method exploits the concepts of phase correlation to compute from the change of image frequencies of a textured plane, distance and slant information. The two different estimates together with structure estimates from classical image motion are combined and integrated over time using an extended Kalman filter. The estimation of the scene structure is demonstrated experimentally in a motion control algorithm that allows the robot to move along a corridor. We demonstrate the efficacy of each individual method and their combination and show that the method allows for visual navigation in textured as well as un-textured environments.},
	keywords = {3D structure information, CAMERAS, Computer vision, extended Kalman filter, Frequency, image frequencies, Image motion analysis, Image texture, Kalman filters, Layout, motion control, Motion estimation, Navigation, Optical computing, phase correlation, piecewise planar scene, Robustness, Simultaneous localization and mapping, Speech processing, textured plane, video signal processing, visual navigation},
	isbn = {978-1-4244-0912-9},
	doi = {10.1109/IROS.2007.4399568},
	author = {Bitsakos,K. and Li Yi and Ferm{\"u}ller, Cornelia}
}
@conference {12033,
	title = {3D motion representations in visual servo control},
	booktitle = {Proceedings of International Symposium on Computer Vision, 1995},
	year = {1995},
	month = {1995/11/21/23},
	pages = {61 - 66},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A new approach to visual servoing and vision-guided robotics is introduced. This approach uses visual information for autonomous behavior. It amounts to using robust, global spatiotemporal representations easily extracted from the dynamic imagery. Specifically, the geometrical patterns of normal flows are used as the input to the servo mechanism. It is shown that the positions of these patterns are related to the three dimensional motion parameters. By locating the positions of these patterns, we can solve a variety of navigational problems with little computational effort},
	keywords = {3D motion representations, autonomous behavior, CAMERAS, computational effort, Computer vision, dynamic imagery, global spatiotemporal representations, image representation, Mobile robots, motion control, Navigation, navigational problems, Optical computing, Robot kinematics, Robot sensing systems, robot vision, Robot vision systems, Robotics and automation, servomechanisms, Servosystems, Spatiotemporal phenomena, vision-guided robotics, visual information, visual servo control, visual servoing},
	isbn = {0-8186-7190-4},
	doi = {10.1109/ISCV.1995.476978},
	author = {Ferm{\"u}ller, Cornelia and LoongFah Cheong and Aloimonos, J.}
}
